NTNU, Norway invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at NTNU, Norway.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Social Work with focus on international migration
We have a vacancy for a 3-year PhD position at the Department of Social Work.
For a position as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree. The Faculty of Social and Educational Sciences will be your employer.
The PhD position is connected to the interdisciplinary field of IMER (International Migrations and Ethnic Relations) and the research group of Migration and welfare: Welfare and migration – Department of social work – NTNU.
The PhD project will address the following research areas:
- International migration
- Migration strategies and policies
- Ethnic relations
The successful applicant will conduct research with emphasis on opportunities and consequences of international migrations. The applicant will be affiliated the research group that consists of researchers within the field of social work, sociology, political science, economics, and anthropology.
Deadline : 15th September 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Social Work with focus on international migration
The attachments (including a description of your scientific work) must accompany the application as these documents form the basis of the application assessment. The documents must be in Norwegian/a Scandinavian language or English.
Please note: the application will only be assessed on the basis of the information we have received by the application deadline. Therefore, make sure that your application clearly shows how your skills and experience meet the criteria described above. The application and all attachments must be sent electronically via Jobbnorge.no. If you are invited to an interview, you must bring certified copies of certificates and diplomas upon request.
The application must include:
- CV, certificates and diplomas
- Transcripts and diplomas for bachelor’s and master’s degrees. If you have not completed the master’s degree, you must submit a confirmation that the master’s thesis has been submitted.
- A copy/pdf of the master’s thesis. If you recently have submitted your master’s thesis, you can attach a draft of the thesis. Documentation of a completed master’s degree must be presented before the application deadline.
- Project proposal: Project description outlining the research questions, academic focus area, theoretical perspectives, methodological design, and progress plan for the project (max. 3,000 characters).
- Name and address of three professional references
- Possibly publications etc. other relevant research work
If all, or parts, of your education has been taken abroad, we also ask you to attach documentation of the scope and quality of your entire education, both Bachelor’s and Master’s education, in addition to other higher education. If your institution uses “diploma supplement” (normal for most European institutions), you must attach this. A description of the documentation required can also be found here. If you already have a statement from Norwegian Directorate for Higher Education and Skills (HK-dir), please attach this as well.
Deadline : 15th September 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Vocal Biomarkers: Acoustics of the Voice for Health Applications
The voice carries subtle acoustic indicators of our health – in some conditions, changes appear years before clinical diagnosis. Can we learn to extract them reliably?
We are looking for a PhD candidate to join the Acoustics group at the Department of Electronic Systems, NTNU, and contribute to the emerging field of vocal biomarkers: measurable features of the voice that can help detect, monitor and follow up health conditions in a non-invasive way.
You will be part of a leading acoustics environment, and of Connection, an interdisciplinary research group under NTNU’s strategic research area NTNU Community, joining an international network of researchers working at the intersection of acoustics, physical modeling, voice and health. During the PhD you will build a sought-after profile spanning acoustics, signal processing and clinical application, in strong demand in both research and industry.
Your immediate leader will be the Head of the Acoustics group. The main supervisor for the position is Associate Professor Sara R. Martín.
Deadline : 1st September 2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Political Science/Public Policy and Administration
Are you motivated to take a step towards a doctorate which opens up exciting career opportunities?
As a PhD Candidate at the Department of Sociology and Political Science you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, both within and outside academia.
This PhD position in Political Science offers an excellent opportunity for scientific development through designing, conducting, and disseminating research on a contemporary topic. The goal of the PhD. position is to complete doctoral-level education and to complete a doctoral degree.
The position is 4 years, including one year of promotion work such as teaching.
The candidate will be part of the active research group Public Policy and Administration at the Department of Sociology and Political Science and be part of a broader international network of researchers working in the field of Public Administration & Management and Digital Government.
Deadline : 1st September 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Causal machine learning for societal impact of migraine treatments
Migraine and primary headaches are among the most common, disabling and costly diseases worldwide. Current clinical practice still relies on heuristic, trial‑and‑error treatment choices, leading to high societal and economic burdens
This project will exploit NorHead’s nation‑wide, high-dimensional and multimodal research database (registry, health surveys, genomics, metabolomics, clinical trials, headache diaries, etc.) in order to create causal‑machine‑learning (causal ML) tools that predict the societal and economic impact of treatment decisions. The candidate will apply causal ML to estimate individualized and population-level treatment effects (ITE/CATE) on outcomes such as sick‑leave days, health‑care utilization, and productivity loss
We are looking for a motivated PhD student who will be part of a methodologically strong, interdisciplinary and international research environment at NorHead. Our collaborators include leading research groups in Norway (NTNU, UiB), the UK, Denmark, and Spain – including the world-renowned High-Dimensional Neurology Group at UCL Queen Square Institute of Neurology in London. More aiD partners include Equinor, Hydro, DNV, Kongsberg Maritime, Elkem and Statsbygg among others
Deadline : 1st September 2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Causal machine learning for societal impact of migraine treatments
Migraine and primary headaches are among the most common, disabling and costly diseases worldwide. Current clinical practice still relies on heuristic, trial‑and‑error treatment choices, leading to high societal and economic burdens
This project will exploit NorHead’s nation‑wide, high-dimensional and multimodal research database (registry, health surveys, genomics, metabolomics, clinical trials, headache diaries, etc.) in order to create causal‑machine‑learning (causal ML) tools that predict the societal and economic impact of treatment decisions. The candidate will apply causal ML to estimate individualized and population-level treatment effects (ITE/CATE) on outcomes such as sick‑leave days, health‑care utilization, and productivity loss
We are looking for a motivated PhD student who will be part of a methodologically strong, interdisciplinary and international research environment at NorHead. Our collaborators include leading research groups in Norway (NTNU, UiB), the UK, Denmark, and Spain – including the world-renowned High-Dimensional Neurology Group at UCL Queen Square Institute of Neurology in London. More aiD partners include Equinor, Hydro, DNV, Kongsberg Maritime, Elkem and Statsbygg among others
Deadline : 1st September 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Electricity Market Coupling and Market Design Transformation
Electricity markets are undergoing a fundamental transformation driven by large-scale renewable integration, stronger interconnections, electrification of demand, hydropower flexibility, storage, and increasing balancing needs. These developments strengthen the interaction between market design and physical power-system operation. In Europe and the Nordic region, the transition from traditional capacity-allocation methods towards flow-based market coupling creates new opportunities, but also new challenges related to price formation, congestion management, transparency, welfare distribution, and operational feasibility.
The objective of this PhD project is to develop new knowledge, models, and analytical insights into how future electricity market designs can support the efficient and secure operation of low-carbon power systems. The project will focus on market coupling, optimal pricing in market clearing, flow-based capacity allocation, interconnectors modelling, and market-design transformation in Nordic and European electricity markets.
The PhD candidate will develop and apply optimization-based market models to analyze how different market-clearing and capacity-allocation methods affect zonal prices, cross-border exchange, congestion rents, welfare distribution, non-fossil flexibility, and system operation. Particular attention will be given to the interpretation of market prices through marginal values, shadow prices, congestion components, scarcity signals, and the relationship between market outcomes and physical network constraints. Regulatory aspects also need to be included through market surveillance and power producer components must embrace the feedback of production aspects.
In flow-based market coupling, market outcomes depend strongly on modelling choices related to the representation of the transmission grid, available cross-zonal capacity, operational security constraints, remedial actions, internal grid constraints, and interconnectors. The project may investigate how such assumptions affect the feasible trading domain, price formation, transparency, congestion management, welfare distribution, and the extent to which zonal prices reflect the marginal value of energy and scarce transmission capacity in the context of electricity market design.
Deadline : 31st August 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Environmental Psychology
The PhD position builds on previous work of researchers from the group “Citizens, Environment and Saftey”. In the EU project ENCHANT, we collected electricity use data of 2400 European households for five weeks and provided psychologically informed interventions to reduce energy consumption through a web-based app. Using machine learning, we then collaborated with a software company and trained an algorithm that can suggest the combination of interventions to households that are expected to result in the best electricity savings, given the socio-psychological profile of the households. The targeted algorithm-suggested interventions were (based on the data from the project) about 50 % more successful in energy saving than the best possible general intervention strategy. In this PhD project, we want to take the next step in this development and use generative AI to tailor the intervention messages and visuals dynamically to user preferences to increase the effect even more and integrate these solutions with smart steering and measurements in pilot households. The task of the PhD will be to work in an interdisciplinary team between psychology and AI experts to help train the tailoring engine, feed it with scientifically solid input and define the boundaries of the space it can operate in. When the prototype is developed, the second part of the PhD will be to test it on households and assess its appeal and effect both qualitatively and quantitatively.
Deadline : 31st August 2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in direct-write nanopatterning of functional materials
A PhD research fellow position in the field of direct-write nanopatterning of functional materials for electronics and photocatalysis is available. The academic position will provide the opportunity for professional development through studies towards a PhD degree in nanotechnology.
The tasks for the PhD research fellow will cover the development of thermal scanning probe lithography as a transformative tool for precision nanostructuring of functional materials, providing maskless, damage‑free patterning with sub‑10 nm control. The nanostructuring will focus on complex oxide thin films, van der Waals films and plasmonic photocatalysts. The position will also involve synthesis of the materials as well as characterization of structural, functional and catalytic properties. The successful candidate will be affiliated to the FACET research group at the Department of Materials Science and Engineering, NTNU and will have a strong collaboration with the Catalysis Group at Department of Chemical Engineering, NTNU and NTNU Nanolab. You will work in a multidisciplinary and international environment. The supervisor team will consist of Professor Mari-Ann Einarsrud, Professor Magnus Rønning and Associate Professor Ingrid Hallsteinsen.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia.
Deadline : 25th August 2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in direct-write nanopatterning of functional materials
A PhD research fellow position in the field of direct-write nanopatterning of functional materials for electronics and photocatalysis is available. The academic position will provide the opportunity for professional development through studies towards a PhD degree in nanotechnology.
The tasks for the PhD research fellow will cover the development of thermal scanning probe lithography as a transformative tool for precision nanostructuring of functional materials, providing maskless, damage‑free patterning with sub‑10 nm control. The nanostructuring will focus on complex oxide thin films, van der Waals films and plasmonic photocatalysts. The position will also involve synthesis of the materials as well as characterization of structural, functional and catalytic properties. The successful candidate will be affiliated to the FACET research group at the Department of Materials Science and Engineering, NTNU and will have a strong collaboration with the Catalysis Group at Department of Chemical Engineering, NTNU and NTNU Nanolab. You will work in a multidisciplinary and international environment. The supervisor team will consist of Professor Mari-Ann Einarsrud, Professor Magnus Rønning and Associate Professor Ingrid Hallsteinsen.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia.
Deadline : 25th August 2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Unified Autonomy across Robot Configurations
Join a nation-wide team: The Norwegian Centre for Embodied AI (NCEI), one of Norway’s six national AI centers, is recruiting outstanding researchers to advance a universal science of embodied intelligence. NCEI brings together leading robotics and AI groups with key partners from industry and the public sector to study how intelligence emerges from the interaction between body, computation, and environment, across flying, ground, and aquatic robot configurations. Our mission is to chart a generalizable path for physical AI and transform how robot morphology and autonomy are co-designed, enabling new generations of systems tailored to their operational environments and missions. Successful candidates will join an international community with world-class facilities and strong collaborations across Norwegian universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier.
Deadline : 25th August 2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Underwater Foundation Models
Join a nation-wide team: The Norwegian Centre for Embodied AI (NCEI), one of Norway’s six national AI centers, is recruiting outstanding researchers to advance a universal science of embodied intelligence. NCEI brings together leading robotics and AI groups with key partners from industry and the public sector to study how intelligence emerges from the interaction between body, computation, and environment, across flying, ground, and aquatic robot configurations. Our mission is to chart a generalizable path for physical AI and transform how robot morphology and autonomy are co-designed, enabling new generations of systems tailored to their operational environments and missions. Successful candidates will join an international community with world-class facilities and strong collaborations across Norwegian universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier.
Deadline : 25th August 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Soft Aerial Robotics and Autonomy
Aerial robotics have scored significant successes yet – interestingly – they have long employed body morphologies that merely represent miniaturizations of manned aviation concepts or have resorted to biomimicry. However, novel missions and environments require that we look into new designs and concepts both for the robot’s morphology as well as its control and autonomy.
To that end, soft bodies offer the possibility to create flying robots with unique capabilities. By exploiting advanced materials, we can achieve anisotropic compliance – offering softness only in the desired directions of interaction – while maintaining light weight and high control authority. Importantly, body softness can be exploited by appropriately designed control in order to achieve enhanced safety and the ability to navigate particularly cluttered and high-risk environments. In turn, deriving such control laws represents its own challenge calling for the utilization of advanced techniques including but not limited to deep reinforcement learning and model predictive control.
Deadline : 25th August 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Fellowship in an interdisciplinary project on Democratic Resilience
The PhD-project is part of an interdisciplinary research project on democratic resilience funded by NTNU Community: https://www.ntnu.edu/community. The project combines normative and empirical perspectives on welfare institutions and redistributive policies as means for strengthening democratic institutions and practices in the face of increasing polarization, decline in political trust and the rise of authoritarian populism. Our focus is on what some call the social model of democratic self-defense. We are interested in what normative commitments democratic government imply, how well strengthening democratic resilience by reducing social and economic inequality aligns with these commitments, and to what extent such an approach is reflected in the public rhetoric of Norwegian political elites.
The PhD research fellow is expected to address conceptual and normative challenges facing the social model of democratic self-defense and to explore what specific policies such a model might imply. In addition, the PhD research fellow will investigate to what extent elected elites in Norway use framing that aligns with the social model, in close cooperation with supervisors.
The precise direction and focus of the PhD project will be developed in the initial period of the fellowship in dialogue between the candidate and the supervisors.
Deadline : 23rd August 2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Sea-Ice Physics and Modelling
Rapid Arctic warming is driving profound changes in sea-ice extent, thickness, and mechanical behavior, with wide-ranging implications for climate feedbacks, ecosystems, infrastructure, navigation, and geopolitics. Despite major advances, large-scale continuum sea-ice models still rely on simplified parameterizations to represent critical sub-grid physical processes such as ridging, floe interaction, jamming, and wave-induced breakup.
Discrete Element Models (DEMs) offer a powerful complementary framework by explicitly resolving ice floes and their mechanical interactions. By allowing deformation, fracture, and redistribution processes to emerge naturally from contact mechanics, DEMs provide a physically grounded basis for improving our understanding of sea-ice dynamics and informing more realistic parameterizations in continuum models.
The overarching ambition of this PhD study is to advance DEM-based sea-ice physics in a way that bridges floe-scale mechanics, continuum sea-ice model parameterization, and operational decision support.
- Develop physically consistent representations of ice ridging within a DEM framework, enabling simulation of ice compression, mass redistribution, and ridge keel formation.
- Incorporate wave–ice interaction processes, with emphasis on wave-induced floe motion, breakup, and rearrangement in the Marginal Ice Zone.
- Derive emergent sea-ice properties (e.g. effective strength, deformation rates, floe-size distributions) and translate these into improved parameterizations for continuum sea-ice models used in climate and forecasting systems.
- Improve computational efficiency and scalability of DEM-based simulations through algorithmic optimization, hierarchical or multi-resolution approaches, and/or reduced-order methods.
- Apply the developed modelling framework to assess mechanical ice hazards relevant for navigation, infrastructure, and Arctic observing systems.
This PhD position is an integral contribution to Arctic Ocean 2050, supporting the program’s ambition to deliver scientifically robust, scalable modelling tools for a rapidly changing Arctic Ocean.
Deadline : 23rd August 2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Sea-Ice Physics and Modelling
Rapid Arctic warming is driving profound changes in sea-ice extent, thickness, and mechanical behavior, with wide-ranging implications for climate feedbacks, ecosystems, infrastructure, navigation, and geopolitics. Despite major advances, large-scale continuum sea-ice models still rely on simplified parameterizations to represent critical sub-grid physical processes such as ridging, floe interaction, jamming, and wave-induced breakup.
Discrete Element Models (DEMs) offer a powerful complementary framework by explicitly resolving ice floes and their mechanical interactions. By allowing deformation, fracture, and redistribution processes to emerge naturally from contact mechanics, DEMs provide a physically grounded basis for improving our understanding of sea-ice dynamics and informing more realistic parameterizations in continuum models.
The overarching ambition of this PhD study is to advance DEM-based sea-ice physics in a way that bridges floe-scale mechanics, continuum sea-ice model parameterization, and operational decision support.
- Develop physically consistent representations of ice ridging within a DEM framework, enabling simulation of ice compression, mass redistribution, and ridge keel formation.
- Incorporate wave–ice interaction processes, with emphasis on wave-induced floe motion, breakup, and rearrangement in the Marginal Ice Zone.
- Derive emergent sea-ice properties (e.g. effective strength, deformation rates, floe-size distributions) and translate these into improved parameterizations for continuum sea-ice models used in climate and forecasting systems.
- Improve computational efficiency and scalability of DEM-based simulations through algorithmic optimization, hierarchical or multi-resolution approaches, and/or reduced-order methods.
- Apply the developed modelling framework to assess mechanical ice hazards relevant for navigation, infrastructure, and Arctic observing systems.
This PhD position is an integral contribution to Arctic Ocean 2050, supporting the program’s ambition to deliver scientifically robust, scalable modelling tools for a rapidly changing Arctic Ocean.
Deadline : 23rd August 2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Artic Maritime Operations AI-Enabled Forecasting and Decision Support
The Marginal Ice Zone represents one of the most complex and operationally challenging environments in Arctic maritime operations. In the MIZ, wave–ice interaction, ice breakup, and subsequent compaction can rapidly alter navigability. Storm events may lead to fast shifts in the ice edge and sudden extension of the MIZ, requiring timely decisions under significant uncertainty.
While satellite observations and ice charts are essential sources of information, their temporal resolution and predictive capability are often insufficient in the MIZ. This motivates the development of short‑term forecasting and scenario‑based tools that explicitly account for fast ice dynamics and uncertainty relevant for operational planning.
The PhD addresses Arctic ice navigation as a system‑level challenge, integrating heterogeneous information from onboard sensors such as marine radar and cameras, satellite Earth‑observation products, ice charts, and metocean forecasts. The objective is to produce coherent, continuously updated representations of ice conditions that support short‑term forecasting, nowcasting, and scenario exploration for route planning and operational decision‑making.
Physics‑based simulation plays a central role in this research project by enabling propagation of ice conditions in time and exploration of physically plausible scenarios when observations are sparse or delayed. In this project, the SAMS framework will be used in an operationally oriented configuration, focusing on computationally efficient simulation of MIZ processes such as wave‑induced ice breakup and ice‑edge evolution.
These simulations will both directly inform forecasting and be used to support AI model training, validation, and interpretability, providing a physically grounded backbone for hybrid AI–physics decision‑support concepts.
AI methods will be applied to fuse heterogeneous data sources, learn fast surrogate representations of physics‑based simulations, and quantify uncertainty relevant for operational decisions. The project leverages MAI foundations for AI‑ready data, hybrid modelling, and trusted AI, while tailoring these capabilities to Arctic MIZ conditions.
Deadline : 23rd August 2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Artic Maritime Operations AI-Enabled Forecasting and Decision Support
The Marginal Ice Zone represents one of the most complex and operationally challenging environments in Arctic maritime operations. In the MIZ, wave–ice interaction, ice breakup, and subsequent compaction can rapidly alter navigability. Storm events may lead to fast shifts in the ice edge and sudden extension of the MIZ, requiring timely decisions under significant uncertainty.
While satellite observations and ice charts are essential sources of information, their temporal resolution and predictive capability are often insufficient in the MIZ. This motivates the development of short‑term forecasting and scenario‑based tools that explicitly account for fast ice dynamics and uncertainty relevant for operational planning.
The PhD addresses Arctic ice navigation as a system‑level challenge, integrating heterogeneous information from onboard sensors such as marine radar and cameras, satellite Earth‑observation products, ice charts, and metocean forecasts. The objective is to produce coherent, continuously updated representations of ice conditions that support short‑term forecasting, nowcasting, and scenario exploration for route planning and operational decision‑making.
Physics‑based simulation plays a central role in this research project by enabling propagation of ice conditions in time and exploration of physically plausible scenarios when observations are sparse or delayed. In this project, the SAMS framework will be used in an operationally oriented configuration, focusing on computationally efficient simulation of MIZ processes such as wave‑induced ice breakup and ice‑edge evolution.
These simulations will both directly inform forecasting and be used to support AI model training, validation, and interpretability, providing a physically grounded backbone for hybrid AI–physics decision‑support concepts.
AI methods will be applied to fuse heterogeneous data sources, learn fast surrogate representations of physics‑based simulations, and quantify uncertainty relevant for operational decisions. The project leverages MAI foundations for AI‑ready data, hybrid modelling, and trusted AI, while tailoring these capabilities to Arctic MIZ conditions.
Deadline : 23rd August 2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD research fellow in STS and sustainability transitions
This PhD position is part of the Research Council of Norway TOPPFORSK project FEASIBILITY: The socio-political and techno-economic craft of enabling twin transitions. TOPPFORSK is among Norway’s most prestigious funding schemes for ground-breaking research, supporting projects with the potential to advance international research frontiers.
FEASIBILITY investigates how societies make large-scale transformations towards climate neutrality and digitalization appear feasible, legitimate and actionable. The project focuses on the intersection of decarbonization and digital transitions, often referred to as the twin transitions. Across Europe, governments, industries and public institutions increasingly rely on scenarios, models, indicators, roadmaps and assessments to guide decisions about energy systems, infrastructure, industry, digital technologies and climate policy.
The project starts from the observation that such tools do not simply describe possible futures. They actively participate in shaping what is considered realistic, desirable and achievable. FEASIBILITY therefore studies how knowledge, values and governance arrangements influence which transition pathways gain credibility and political traction, and which are marginalized or dismissed.
Bringing together perspectives from Science and Technology Studies, sustainability transitions research, political science, geography and techno-economic analysis, FEASIBILITY includes comparative empirical studies across several European countries (Norway, Germany, Portugal, and the United Kingdom). The successful candidate will contribute to a growing international research frontier concerned with the role of expertise, knowledge production, valuation and future-making in societal transformations, primarily through qualitative methods.
Deadline : 20th August 2026
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD in Predictive AI-Based Maintenance and Optimization of Building Energy Management Systems
Are you passionate about artificial intelligence, digital twins, and energy management systems in buildings? We are offering a fully funded, three-year PhD position to develop a cutting-edge AI decision-support tool for predictive maintenance and optimization of building energy management systems and technologies (i.e., Heating, Ventilation and Air Conditioning – HVAC). Partnering with Statsbygg, the Norwegian Government’s Building Agency, your research will be deployed across a massive portfolio of public buildings. Moving beyond outdated calendar-based maintenance schedules, your mission is to solve a critical economic and environmental timing problem: knowing exactly when it becomes cost-optimal to intervene in degrading or overloaded systems. You will overcome the scarcity of real-world fault data by combining Simulation-Based Inference (SBI) and Building Performance Simulation (BPS). By integrating historical facility management (FDVU) records with live building sensor data, you will train AI models to recognize abnormal performance patterns, quantify quality-adjusted service life, and autonomously recommend whether a system needs maintenance, recalibration, or a capacity upgrade.
Deadline : 16th August 2026
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate position: Hybrid AI-Optimization for Dynamic and Stochastic Transport Operations
We invite applications for a PhD Candidate position focused on the development of hybrid artificial intelligence and optimization methods for dynamic and stochastic transport operations. The position is based at the Department of Industrial Economics and Technology Management, located at NTNU’s campus in Trondheim.
This is an educational position, which will provide promising research recruits the opportunity for professional development through studies towards a PhD-degree. The position is connected to the PhD program at the Faculty of Economics and Management, and the faculty will be your employer.
Transport and logistics systems are becoming increasingly complex, data-rich, and dynamic. Public transport operators, freight carriers, and logistics providers must make decisions under uncertainty while responding to disruptions, fluctuating demand, and changing operating conditions.
This PhD project focuses on developing AI-supported decision methods for routing and scheduling in transport and logistics systems. The research investigates how operations research and artificial intelligence can be combined to improve planning and operational decision making under uncertainty.
The project addresses challenges that arise when traditional optimization models are applied in real-world settings characterized by large-scale networks, stochastic demand, operational disruptions, and complex constraints. A central research question is how machine learning can be integrated with optimization algorithms to support faster, more robust, and more adaptive decision making.
The project addresses research questions like:
- How can learning-based models be integrated with optimization methods to improve the robustness and performance of transport planning decisions?
- How can hybrid AI–optimization methods scale to large real-world transport and logistics systems while handling the many constraints encountered in practice?
- How can routes and schedules be evaluated efficiently across many possible future scenarios to support both tactical planning and real-time adaptation?
Deadline : 16th August 2026
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in prospective sustainability assessment of novel marine natural products
For a position as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree.
The PhD Candidate position is part of AIMARIA – Accelerating Innovation in Marine AI-powered Bioprospecting. AIMARIA is a large European research project focused on the discovery and demonstration of sustainable production routes for high-value marine bioactive compounds. The project involves several universities, research institutes, and companies across Europe and New Zealand, and is coordinated by NTNU, Department of Biotechnology and Food Science.
The PhD position shall deliver actionable recommendations for sustainability, circularity and social acceptance of synthesis pathways, enzymes and compounds. The work shall embed environmental, economic and societal life-cycle performance to design and decision-making for production marine bioactives through prospective life cycle assessment and techno-economic analysis. The candidate shall also contribute to address societal expectations and ethical use of marine resources through engagement with indigenous, regulatory and societal stakeholder perspectives.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia.
IndEcol is a pioneer in the development and application of industrial ecology methods, as well as the use of large data sets and scientific computing in industrial ecology. We focus on understanding resource use and environmental pollution associated with human activities, assessing the environmental aspects of different technologies and modeling society’s use of materials. IndEcol combines world-class competence in Life Cycle Assessment (LCA), Input-Output analysis (I/O), and Material Flow Analysis (MFA) with scientists’ contribution to IPCC and IPBES assessment reports, the International Resource Panel, active participation to the Life-Cycle Initiative hosted by UN Environment, and numerous publications featured in high-ranked journals. IndEcol hosts its own International MSc program and contributes to NTNU’s engineering education. The research team consisting of eight faculty members, the Industrial Ecology Digital Laboratory, and about 70 researchers, Post Docs. and PhD students. More information can be found at https://www.ntnu.edu/indecol.
Deadline : 15th August 2026
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in blast wave propagation in soil and rock, and structure interaction
The project investigates whether existing civil infrastructure can provide adequate protection against blast and impact threats arising from modern warfare scenarios, including missile and drone attacks. Particular emphasis will be placed on understanding structural response, failure mechanisms and collapse resistance of typical building structures, underground facilities and other critical infrastructure.
The research will combine advanced finite element simulations, blast loading models, propagation of pressure waves in the ground and soil–structure interaction analyses to establish methods for assessing the protective capacity of existing infrastructure and identifying effective strengthening measures. Selected existing structures will be used as case studies.
A central aspect of the project is to identify how wave propagation in soil and soil–structure interaction influences the vulnerability or robustness of infrastructure. The candidate will investigate how soil stiffness, layering, and water saturation conditions modify the distribution of blast loads and affect potential failure mechanisms. This includes studying scenarios where underground structures provide shielding effects, as well as cases where wave reflection or confinement may increase local damage. In addition to numerical research, the PhD candidate will contribute to experimental investigations of geomaterials subjected to high strain rates. Laboratory testing will be used to characterize the dynamic properties of soil and rock, providing essential input for model calibration and validation.
Deadline : 15th August 2026
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Computational Mechanics of Protective Structures under Blast and Impact Loading
The project investigates whether existing civil infrastructure can provide adequate protection against blast and impact threats arising from modern warfare scenarios, including missile and drone attacks. Particular emphasis will be placed on understanding structural response, failure mechanisms and collapse resistance of typical building structures, underground facilities and other critical infrastructure.
The research will combine advanced finite element simulations, blast loading models and fluid–structure interaction analyses to establish methods for assessing the protective capacity of existing infrastructure and identifying effective strengthening measures. Selected existing structures will be used as case studies.
The project addresses emerging challenges related to civil preparedness and protection of critical infrastructure in a rapidly changing security environment. The project is carried out with an accompanying PhD project in geotechnics that will focus on propagation of pressure waves in the ground and underground structures when subjected to detonations.
Deadline : 15th August 2026
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in AI-assisted asset management of coastal infrastructure
The candidate will be working on the project ”AI-empowered asset management for port structures”.
Norwegian ports are critical infrastructure — vital to national logistics, maritime trade, and the livelihoods of coastal communities. Yet unlike roads and bridges, most port structures fall outside any regulated asset management system: there are no common inspection guidelines, no shared database of structural condition, and no systematic basis for assessing how resilient these assets are to hazards. As climate change drives increases in extreme weather, storm surges, and flooding along the Norwegian coast, this gap is becoming increasingly consequential.
This PhD project addresses that challenge head-on by developing an AI-powered asset management framework for Norwegian port structures. The approach leverages the BRUTUS database — Norway’s national asset management system for ferry quays, containing labelled damage data as training material for a machine learning model capable of automatically classifying structural
damage from drone inspection images. The project aims to bring automated, cost-effective damage detection to port structures that have historically lacked any formal monitoring.
The research is anchored at the Magerholm Research Quay (MRQ), a unique full-scale laboratory established as part of Fjordlab Ålesund through a collaboration between Møre og Romsdal County and NTNU. MRQ provides unparalleled access to real-world structural and operational data, including met-ocean conditions and ferry impact loads — an ideal testbed for developing and validating the AI and drone-based inspection methods at the core of this project.
Deadline : 15th August 2026
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Psychology
The position is affiliated with the “Trondheim Early Secure Study (TESS)”, a longitudinal study of psychosocial development, mental health, and health behaviors. Approximately 1,000 children have been assessed every second year since the age of four. The participants are now 22 years old, and the tenth wave of data collection is ongoing.
The PhD project focuses on how attachment develops from childhood into early adulthood. Using longitudinal data from TESS, including repeated assessments of attachment and detailed information on psychological development, mental health, and psychosocial functioning, the project will identify developmental trajectories of attachment and examine factors that may predict these trajectories. The overarching aim is to advance our understanding of the factors that shape different attachment pathways across childhood and adolescence
The PhD candidate will be part of the TESS research group, which consists of 10 researchers, 2 postdoctoral fellows, and 10 PhD candidates, in addition to 4 research assistants responsible for data collection. The candidate will be supervised by Associate. Prof. Kristine Rensvik Viddal. The candidate will further be co-supervised by at least one of the senior researchers in the TESS group Prof. Lars Wichstrøm (PI of TESS) and Prof. Silje Steinsbekk (co-PI of TESS) while also collaborating with international researchers.
Deadline : 14th August 2026
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Nanophotonics for Health Monitoring
We are searching for a creative, skilled and ambitious candidate for our activities on the optics of wearable metasurfaces with applications in health monitoring.
The aim of the candidate is to fabricate and characterize wearable optical metasurfaces and photonic crystals. Main focus will be the understanding of the complex interactions of light with photonic devices as well as the in vivo characterization using advanced spectroscopic modules. We will investigate the potential of these structures in detection of vital signals pushing the limits of non-invasive cardiovascular monitoring using nanophotonics.
The research will be performed at the Department of Electronic Systems, NTNU in close collaboration with scientific partners in St’ Olavs, Department of Circulation and Medical Imaging and Department of Mechanical and Industrial Engineering.
Deadline :10th August 2026
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in synthetic biology and biosynthetic gene cluster expression
We have a vacancy for a full-time PhD candidate at the Department of Biotechnology and Food Science, NTNU. The position is linked to AIMARIA – Accelerating Innovation in Marine AI-Powered Bioprospecting, a Horizon Europe Innovation Action coordinated by NTNU.
AIMARIA combines omics, bioinformatics, artificial intelligence, synthetic biology, bioprocess development and sustainability assessment to accelerate the discovery and sustainable production of marine and aquatic natural products. At NTNU, a central part of the project is the development of more predictable and efficient methods for the refactoring and heterologous expression of biosynthetic gene clusters (BGCs).
The PhD project will investigate how regulatory DNA elements, BGC architecture and microbial host selection influence the expression of complex biosynthetic pathways. Molecular biology, microbial strain engineering and synthetic biology will be combined with AI-supported design of promoters, 5′ untranslated regions and other regulatory elements. The resulting data will be used to establish more predictable design principles for BGC expression and to support iterative AI-guided design–build–test–learn cycles.
The precise scope of the doctoral project will be refined according to the candidate’s qualifications, the BGCs prioritised in AIMARIA and the scientific development of the project.
The PhD candidate will work closely with a researcher recruited to the same scientific activity and with national and international partners in the AIMARIA consortium. The project will provide training at the interface between fundamental research in microbial gene regulation and applied development of microbial production systems for marine natural products and enzymes.
Deadline : 9th August 2026
(29) PhD Degree – Fully Funded
PhD position summary/title: 3-årig ph.d. – stipendiatstilling i teknologi og vitenskapsstudier (STS)
Institutt for tverrfaglige kulturstudier (KULT) ved NTNU har en ledig treårig ph.d.-stilling i prosjektet AMICA som vil utforske helseteknologi i et STS-perspektiv.
Denne stillingen er tilknyttet forskningsgruppen DigiKULT, som arbeider med digital teknologi og samfunnsendringer. Forskningsgruppen har sin forankring i teknologi og vitenskapsstudier (STS) og utforsker det gjensidig formende forholdet mellom teknologi og samfunn. Tematisk sett har forskningsgruppen en bred portefølje og arbeider tverrfaglig med forskningsmiljøer innenfor teknologi og helse. Stillingen vil ha et særlig samarbeid med Senter for omsorgsforskning ved NTNU. Gruppen jobber også bredt internasjonalt med prosjekter og tilknyttede forskere i Europa og internasjonalt.
Doktorgradsprosjektet er tilknyttet det nordisk-baltiske NORDFORSK samarbeidsprosjektet «AI for Mindful Care and Ageing» (AMICA), som utforsker hvordan digital sårbarhet konstrueres, forhandles og praktiseres når KI brukes i hjemmetjenester. De overordnede målene for prosjektet er å undersøke hvordan digital teknologi som KI transformerer praksiser, relasjoner og opplevelser i omsorgsarbeid og velferdstjenester, samt utvikle teoretisk forståelse av hvordan sårbarhet formes av kryssende sosiale identiteter, livsløpshistorier og strukturelle forhold. Prosjektet kombinerer STS med kritiske samfunnsvitenskapelige perspektiver og kritisk gerontologi.
Stipendiaten som ansettes vil fokusere på eldre sine erfaringer med og tilpasning til kunstig intelligens i hjemmebaserte omsorgstjenester. Stipendiaten vil være ansvarlig for å rekruttere og gjennomføre kvalitative intervjuer med eldre for å kartlegge deres livshistorier knyttet til teknologi, blant annet ved bruk av narrative tidslinjer for å utforske og systematisere deltakernes erfaringer. Et sentralt tema for ph.d.-ens tematikk er hvordan KI oppleves i hjemmetjenester, og hvordan digital sårbarhet kan forstås bedre. I tillegg vil stipendiaten basert på innsamlet data ha en sentral rolle i gjennomføringen av workshops og sammen med andre prosjektdeltakere utvikle en vitenskapelig sosioteknisk utstilling basert på resultater fra det overordnede prosjektet. Stipendiaten vil også ha et forskningsopphold ved en av AMICA sine partnerinstitusjoner. Stipendiaten vil ha Institutt for tverrfaglige kulturstudier i Trondheim som sitt daglige arbeidssted.
Deadline : 7. august 2026
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Multi-scale, -physics, -fidelity wind modelling for wind farms
This PhD position is part of the WP4 Digital twin and asset management concerning multiscale wind simulation that involves modelling wind flow across various spatial and temporal scales to improve accuracy. It aims to combine small-scale atmospheric phenomena, such as turbulence, with larger-scale weather patterns and climate influences. These simulations are essential for optimizing the design and placement of wind turbines in complex terrains. Multiscale approaches utilize advanced computational techniques, including nested grids and adaptive mesh refinement. Accurate wind simulation at multiple scales helps in better predicting energy production and reducing operational risks. Some relevant key words (see FME-NorthWind webpage for more details):
· Hybrid Multiscale Modelling
· Hybrid Physics-Machine Learning Analyses
· Coupled Atmosphere-Turbine Models
· Uncertainty Quantification in Hybrid Frameworks
We offer the opportunity to work in a very inspiring and motivating multidisciplinary research group to achieve the goal which is a completed doctoral education up to an obtained doctoral degree.
Deadline : 3rd August 2026
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Measuring Uncertainty and Risk with Agentic AI for Decision-Oriented Modelling
Many critical decisions in science and engineering, from managing power grids to planning subsurface energy operations, depend on computational models that are inherently uncertain. Uncertainty quantification (UQ) seeks to characterize and propagate this uncertainty but translating it into actionable risk measures remains computationally demanding and methodologically challenging. In realistic applications, uncertainty arises from multiple sources, including parametric uncertainty, model-form and structural assumptions, numerical discretization, and incomplete or noisy observations. Addressing these uncertainties typically relies on ensemble-based simulations using computationally expensive high-fidelity models.
This PhD project addresses this challenge by researching agentic programming frameworks for decision-oriented uncertainty and risk quantification, in which multiple specialized AI agents collaborate to design, execute, and evaluate UQ workflows under computational constraints. Rather than automating predefined pipelines, the agents act as meta-decision-makers, reasoning about modelling assumptions, approximation levels, and the choice of risk measures considering both decision objectives and available resources.
The central scientific aim of the PhD project is to establish formal links between uncertainty representations, risk measures, and downstream decisions, and to study how agentic reasoning can navigate trade-offs between interpretability, statistical reliability, and computational feasibility – and to develop principled criteria for when a given risk measure is well-founded given available data and resources, and when alternative approximations should be considered.
Deadline : 3rd August 2026
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Measuring Uncertainty and Risk with Agentic AI for Decision-Oriented Modelling
Many critical decisions in science and engineering, from managing power grids to planning subsurface energy operations, depend on computational models that are inherently uncertain. Uncertainty quantification (UQ) seeks to characterize and propagate this uncertainty but translating it into actionable risk measures remains computationally demanding and methodologically challenging. In realistic applications, uncertainty arises from multiple sources, including parametric uncertainty, model-form and structural assumptions, numerical discretization, and incomplete or noisy observations. Addressing these uncertainties typically relies on ensemble-based simulations using computationally expensive high-fidelity models.
This PhD project addresses this challenge by researching agentic programming frameworks for decision-oriented uncertainty and risk quantification, in which multiple specialized AI agents collaborate to design, execute, and evaluate UQ workflows under computational constraints. Rather than automating predefined pipelines, the agents act as meta-decision-makers, reasoning about modelling assumptions, approximation levels, and the choice of risk measures considering both decision objectives and available resources.
The central scientific aim of the PhD project is to establish formal links between uncertainty representations, risk measures, and downstream decisions, and to study how agentic reasoning can navigate trade-offs between interpretability, statistical reliability, and computational feasibility – and to develop principled criteria for when a given risk measure is well-founded given available data and resources, and when alternative approximations should be considered.
Deadline : 3rd August 2026
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Circumventing bird collision risk, an aerodynamic approach
This PhD position is part of the WP5 Sustainable wind development and related to bird collision risks which is one of the main environmental impacts of onshore and offshore wind turbines on birds. In an offshore setting, many seabird populations are in sharp decline due to climate change and anthropogenic marine activities. There exists yet little knowledge on the extent of collision mortality internationally, due to the difficulty in recording such collision events offshore. Also, existing collision risk models are simplistic in nature and heavily rely on accurate knowledge of avoidance in birds. The PhD will develop an aerodynamic bird collision avoidance model, combining computational fluid dynamics (CFD) of the flow around wind turbines with the aerodynamic characteristics of flying birds to predict collision risk. This model will be fed by existing empirical data on bird flight behavioural responses to wind turbines from various bird radar studies. This model will allow simulation of birds flying within the turbulent airspace of a wind farm, and prediction of the probability of collision at wind turbines. This will be integrated into a Digital Twin (DT) framework to realize augmented reality to inform stakeholders of future environmental impacts of the planned wind farms.
Deadline : 3rd August 2026
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD candidate in fabrication of novel quantum materials
A PhD position in the field of functional materials for future quantum technology is available at the Department of Materials Science and Engineering at the Norwegian University of Science and Technology.
The project aims to fabricate novel quantum materials that unify topological and magnetic properties, enabling completely new interface functionalities in future spintronics.
The project combines elements from physics (magnetic and topological properties), chemistry/material technology (fabrication of nanomaterials) and electronics (quantum devices). This project is interdisciplinary, and the PhD candidate will be part of a larger group using pulsed laser deposition as a tool to engineer new quantum materials. The group is within the Functional Materials and Materials Chemistry Research Group (FACET – http://www.ntnu.edu/ima/research/facet).
Deadline : 2nd August 2026
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in biodiversity impact assessment development
Life Cycle Impact Assessment (LCIA) has seen a tremendous development in the last decades, both in terms of impact coverage and model complexity. However, there are still some impacts that remain uncovered and details to existing impact categories can be continuously added. This PhD position aims to investigate a new impact pathway that is so far uncovered, namely the impact of particulate matter emissions on (plant and/or animal) species diversity.
In addition, insects, even though tremendously important for functioning ecosystems, are so far not included in existing impact categories. This project aims to include insects into one or several of the existing and relevant impact categories, such as land occupation or climate change.
Deadline : 1st August 2026
(36) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Plastics Pollution Modelling and Life Cycle Assessment
Plastic pollution is escalating across all environments and is recognized as a major One Health challenge. However, impacts on freshwater and terrestrial biodiversity are still largely unassessed in a life cycle assessment (LCA) context. At the same time, micro- and nanoplastic exposure in humans is confirmed, but the health implications remain poorly understood and no metrics exist to represent these effects in LCA. This project addresses both gaps in parallel by developing the first integrated One Health modelling framework for plastics. We will build a multi-compartment fate model for terrestrial and freshwater systems, derive the first Characterization Factors for plastic impacts on biodiversity beyond the ocean, and translate biomedical evidence into responsible early-stage human health indicators compatible with LCA. The PhD`s work will focus on both environmental transport modelling and biodiversity impact assessment, in close collaboration with interdisciplinary project partners.
The project is funded through NTNU and is an interdisciplinary project between the Industrial Ecology Programme at the Department of Energy and Process Engineering and the Department of Neuromedicine and Movement Science.
Deadline :1st August 2026
(38) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Advanced High-Temperature Heat Pump Systems
The Department of Energy and Process Engineering at the Norwegian University of Science and Technology has a vacancy for a PhD candidate in Advanced High-Temperature Heat Pump Systems. The position focuses on developing advanced solutions for multi-level recovery and upgrading of industrial waste heat using absorption-based high-temperature heat pump technologies. The PhD candidate will be part of a dynamic and collaborative research environment working on combined heat and power.
The project offers opportunities to contribute to research with strong relevance for the decarbonization of industry, while gaining expertise in modelling, analysis, and potentially experimental investigation of advanced heat pump systems.
The successful candidate will work closely with academic staff and may collaborate with industrial partners, providing valuable experience and strong career development opportunities within energy and process engineering.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia.
Deadline : 1st August 2026
(39) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in AI-Supported Decision-Making for Asset Management of Public Building Portfolios
The PhD project is part of a broader effort to develop AI-supported decision frameworks for public asset management, with a focus on improving evaluation, prioritization, and long-term planning of building portfolios. The work will explore how AI can support more consistent and data-informed assessments while maintaining a human-in-the-loop approach where expert judgment remains central.
The project is a collaboration between the Department of Civil and Environmental Engineering at NTNU, SINTEF Digital, and the Department of Engineering Cybernetics. It is conducted in close collaboration with Statsbygg and other relevant public building owners and authorities.
The research is embedded in the Norwegian Center on AI for Decisions (aiD), ensuring strong interdisciplinary collaboration between academia, research institutes, and public sector partners.
Deadline : 31st July 2026
(40) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in evolutionary genomics of cryptic coloration in frogs
The NTNU University Museum is seeking a highly qualified, ambitious, and motivated PhD candidate for a project focusing on the genomics of cryptic coloration in anurans (frogs and toads).
Anurans display a diversity of colors and patterns that enable them to avoid detection by predators through background matching, masquerade (e.g., resembling leaves or bird droppings), or disruptive coloration. In many cryptically colored species, multiple color morphs co-occur within populations, and similar polymorphisms have evolved repeatedly across distantly related taxa. Proposed mechanisms for the maintenance of such polymorphisms include spatial and temporal variation in selection, heterozygote advantage, and pleiotropic effects linking coloration to other fitness-related traits.
The PhD project will generate and analyze genomic and transcriptomic datasets derived from both samples collected in the field (e.g., Ethiopia, Borneo), and tissue collections in natural history museums. Using population and comparative genomics approaches, the candidate will have opportunities to investigate the evolutionary mechanisms underlying (i) the maintenance of cryptic color polymorphisms within species, and/or (ii) the repeated evolution of similar cryptic color traits across lineages. While the project will primarily be computational and lab-based, there will be opportunities to develop complementary field experiments. The focal study system will be defined in collaboration with the successful candidate.
The successful candidate will be employed at the NTNU University Museum’s Department of Natural History. The NTNU University Museum is the natural and cultural history museum of the Norwegian University of Science and Technology in Trondheim. The Department of Natural History conducts research in systematics and taxonomy, evolutionary genomics, as well as in phylogeography, population genetics, and ecology with an emphasis on conservation biology.
Deadline : 30th July 2026
(41) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Method Development in Quantum Chemistry
The position involves the theoretical development and implementation of advanced electronic structure methods in the areas of molecular response theory for molecules in complex environments. The developed methodologies will, for instance, be applied to model advanced spectroscopies. The candidate will be a part of the eT program developer team (www.etprogram.org) and significant parts of the work will be devoted to software development.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia.
Deadline : 28th July 2026
(42) PhD Degree – Fully Funded
PhD position summary/title: PhD position in AI-enabled marine biological monitoring
COD-SPAWN is an ambitious, cross-disciplinary research initiative at NTNU Ålesund that develops and pilots a next-generation autonomous monitoring system for Atlantic cod spawning and pollution dynamics in Borgundfjorden — Norway’s second most important cod spawning ground and a nationally prioritized site for sediment remediation.
The project employs and further develops our advanced AI-based system for marine plankton and particle analyses alongside a suite of marine water quality sensors. In addition to the use of more traditional sampling methods from shore and boats, it deploys unmanned surface vehicles (USVs) equipped with image-based particle sensors and AI-driven data analysis pipelines to enable real-time, high-resolution detection of cod eggs, microplastics, and suspended contamination in a fjord that has historically been compromised by pollution.
Deadline : 26th July 2026
About NTNU- Norwegian University of Science and Technology, Norway- Official Website
The Norwegian University of Science and Technology is a public research university in Norway with the main campus in Trondheim and smaller campuses in Gjøvik and Ålesund. The largest university in Norway, NTNU has over 8,000 employees and over 40,000 students. NTNU in its current form was established by the King-in-Council in 1996 by the merger of the former University of Trondheim and other university-level institutions, with roots dating back to 1760, and has later also incorporated some former university colleges. NTNU is consistently ranked in the top one percentage among the world’s universities, usually in the 101–500 range depending on ranking.
NTNU has the main national responsibility for education and research in engineering and technology, and is the successor of Norway’s preeminent engineering university, the Norwegian Institute of Technology (NTH), established by Parliament in 1910 as Norway’s national engineering university. In addition to engineering and natural sciences, the university offers higher education in other academic disciplines ranging from medicine, psychology, social sciences, the arts, teacher education, architecture and fine art. NTNU is well known for its close collaboration with industry, and particularly with its R&D partner SINTEF, which provided it with the biggest industrial link among all the technical universities in the world. The university’s academics include three Nobel laureates in medicine, Edvard Moser, May-Britt Moser and John O’Keefe.
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