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- Norwegian University of Science and Technology, Norway.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Plastics Pollution Modelling and Life Cycle Assessment
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. The PhD candidate will contribute to the development of novel modelling approaches to understand the environmental fate and impacts of plastic pollution in Norway.
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.
Deadline : 1st August 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in AI-Supported Decision-Making for Asset Management of Public Building Portfolios
Are you motivated to pursue a doctorate while working on real-world challenges in public asset management and decision-making? As a PhD Candidate in this position, you will work towards your doctoral degree while gaining valuable experience in applying artificial intelligence and data-driven methods to complex decision-support processes.
The project focuses on developing AI-supported approaches to improve how large public building portfolios are evaluated and managed. You will work with real use cases and contribute to research that aims to make decision processes more consistent, transparent, and evidence-based.
You will collaborate with a broad network of internal and external partners, including practitioners, domain experts, and researchers. This provides a unique opportunity to combine academic research with practical application in a highly relevant societal context.
Deadline : 31st July 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in green flotation chemistry for sustainable mineral processing – IV-27/26
The Department of Geosciences (IGV) at the Norwegian University of Science and Technology (NTNU) has a vacancy for a full-time PhD Candidate in sustainable flotation chemistry and mineral processing. The successful candidate will join an international and interdisciplinary research environment at the Mineral Processing Laboratory at NTNU, working closely with researchers, PhD candidates, and industrial partners focused on sustainable mineral technologies for the future.
The Mining Engineering research group at NTNU provides a dynamic and collaborative working environment with access to advanced laboratory infrastructure and strong links to academia and industry. The position offers excellent opportunities for scientific development, international collaboration, conference participation, and contribution to research supporting the green and circular transition.
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 : 10th July 2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Mining Engineering – IV-26/26
The project focuses on developing an advanced, AI-integrated methodology for adaptive underground stope dimensioning to address the limitations of traditional empirical approaches. While underground stope design is critical for safe and efficient ore extraction, it is often constrained by sparse geotechnical data. Conventional interpolation techniques frequently struggle with site-specific conditions such as structural anisotropy and in-situ stress variations leading to either overly conservative designs or unplanned dilution.
To overcome these challenges, this project will embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields will feed into an automated stope design workflow, producing variable-length, locally adaptive geometries that optimize the balance between stability, recovery, and dilution control.
Through collaboration with national and international stakeholders, the project aims to establish a validated, data-driven design framework. Key outcomes will include an open-source Python toolkit, measurable improvements in stope performance, and practitioner guidelines for next-generation underground mine planning.
The successful candidate will become part of the newly established MIN30 initiative at the NTNU Mineral Centre, a strategic research initiative focused on securing the future supply of critical minerals through sustainable and innovative technologies. The centre covers the entire mineral value chain, including exploration and resource characterization, mineral processing and extraction, by-product utilization, recycling, metal production, and environmental aspects associated with sustainable and responsible resource development. The MIN30 initiative will recruit several PhD candidates working on both fundamental studies and industrial applications related to future mineral and mining technologies and methodologies. The candidate hired in this position will be integrated with other PhD candidates and researchers within the centre.
Deadline : 10th July 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Methodology Development for Utilisation of Mining Waste (waste rock and tailings)
The Department of Geosciences (IGV) at the Norwegian University of Science and Technology (NTNU) has a vacancy for a full-time PhD Candidate in Methodology Development for Utilisation of Mining Waste (waste rock and tailings) – IV-28/26. The successful candidate will join an international and interdisciplinary research environment within the Mining Engineering Research Group at NTNU, working closely with researchers, PhD candidates, and industrial partners focused on sustainable mineral technologies for the future.
The Mining Engineering Research Group at NTNU provides a dynamic and collaborative working environment with access to advanced laboratory infrastructure and strong links to academia and industry. The position offers excellent opportunities for scientific development, international collaboration, conference participation, and contribution to research supporting the green and circular transition.
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 : 10th July 2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Secure and Trustworthy Data Sharing for Maritime AI Model Development
Digitalization and artificial intelligence (AI) are transforming society, and the integration of AI into the maritime industry represents a digital shift that will redefine today’s operations. The maritime industry is increasingly embracing AI, enabling a broad digital transformation. Data sharing between maritime stakeholders is a key enabler for accelerating the operationalization of AI across maritime value chains. This PhD project therefore calls for research on secure and trustworthy data sharing.
The development and refinement of maritime AI models depend on access to large amounts of high-quality operational data. However, data sharing across organisational and sectoral boundaries is often constrained by concerns related to data ownership, confidentiality, cybersecurity risks, and trust. These challenges represent barriers to the safe and responsible use of AI in the maritime domain.
This PhD project will investigate how cybersecurity risks influence data sharing for maritime AI model development. The project will have an interdisciplinary approach, combining insights from information security, maritime operations, data engineering, and data governance.
The research will include structured review of relevant literature, exploratory studies with industry partners and the development of methods or frameworks to support secure and trusted data sharing for maritime AI model development. Depending on the candidate’s background and the project’s progression, the research may include methods such as anomaly detection techniques or simulation-based testing.
This position offers the opportunity to work at the intersection of cybersecurity, data sharing, and maritime AI in close collaboration with academic and industrial partners. The research outcomes are expected to contribute knowledge on cybersecurity-related barriers and frameworks to support secure and trustworthy data sharing across maritime AI use cases.
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 : 30th June 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Machine Learning Approaches to Conservation Condition Needs of Historic Buildings
For a position as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree.
We are looking for a PhD candidate for one of 13 PhD positions in Marie Sklodowska-Curie Action (MSCA) CHARM (Conservation of Heritage Architecture, buildings and sites by Resilient Methods: hydro-climate factors) project. The main objective of CHARM MSCA Doctoral Network project is to develop new sustainable conservation and restoration solutions, adapted to the current and future climate conditions, designed in a circular economy philosophy, having a low environmental footprint and high handprint. CHARM also responds to socio-economical requirements of wellbeing and proposes conservation/restoration solutions economically acceptable and respecting the cultural value of buildings.
CHARM proposes a holistic approach including:
- Site observation and data collection
- Data preparation to be used by AI, models, etc
- Methods for understanding and modelling past and future degradation of architectural heritage
- Sustainable conservation solutions to several observed problems
- Assessment of the impact of architectural conservation on the environment and of the environment (past, present and future) on architectural heritage
- Bidirectional interactions with society proposing risk management plans but also taking into account the society wills and needs
Deadline : 29th June 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Computational Porous Media Physics
Are you motivated to take a step towards a doctorate and open 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.
We have a vacancy for a PhD candidate in Computational Porous Media Physics at PoreLab, Department of Physics, NTNU. The appointment has a duration of 3 years with the possibility of extension for teaching duties in agreement with the department. The PhD candidate should start no later than October 2026.
Deadline : 21st June 2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Data-driven optimization for Energy Communities
The Department of Engineering Cybernetics at NTNU is offering upto 2 fully funded PhD positions in the area of data-driven decision-making for energy communities.
Energy communities, where groups of households share renewable generation and a common battery storage system, represent a promising concept for increasing renewable integration and local energy self-sufficiency. However, achieving reliable and efficient operation in such systems requires efficient energy management strategies.
This project explores new methodological frameworks that use data-driven and learning-based techniques to enable intelligent, real-time decision-making in interconnected systems, such as in energy communities. The candidates will investigate how real-time data can support new operating models, and collaboration mechanisms to ensure efficient, reliable, and sustainable use of shared offshore energy resources.
The successful candidates will be a part of a dynamic and internationally connected research environment at the Department of Engineering Cybernetics, renowned for its leading expertise in optimization, control, and artificial intelligence for cyber-physical systems.
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.
The PhD candidates will be supervised by Associate Professor Dinesh Krishnamoorthy (Department of Engineering Cybernetics), and co-supervised by Professor Pedro Crespo del Granado (Department of Industrial Economics and Technology Management).
Deadline : 19th June 2026
(10) 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 : 18th June 2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Reliability of Autonomous Systems
This position is sponsored by the European Defence Fund via the SALUBRIS project (Strategic Autonomous Logistics for Uncrewed Battlefield and Intervention System). The successful candidate will contribute to the success of the project by applying cutting edge and innovative approaches to the use cases presented by the project.
The SALUBRIS project focuses on multimodal CASEVAC, employing a mix of unmanned vehicles in different domains (land, sea, air) to perform casualty detection, recovery, and evacuation, through swarm coordination across the domains. Furthermore, the project employs sensors data fusion from available sources, including both wearable and nearable sensors, to provide autonomous triage based on the START methodology, employing explainable AI techniques to prioritize the casualties.
The successful candidate will be involved in the reliability aspects of the autonomous systems presented by the project, particularly with aspects related to the design of fault tolerance solutions in the overall architecture, and on evaluating the robustness of machine learning and data fusion solutions.
Deadline : 15th June 2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Optimal Utilization of Power Components and Grids using Digital Twin
We are seeking a motivated PhD candidate to work on hybrid analysis and modeling for next-generation digital twins in the electric power grid. The position is part of the UPGriD project, which brings together leading research and industry partners to develop new tools for real-time dynamic thermal rating, virtual sensing, anomaly detection, and predictive modeling of power-grid components.
The candidate will join an interdisciplinary research environment combining artificial intelligence, cybernetics, numerical modeling, and electric power engineering. The work will be carried out at NTNU in close collaboration with SINTEF Energy Research, and industrial partners from the power and energy sector. The PhD will work across several technical work packages and contribute to methods that can be applied to transformers, power cables, and distribution substations.
The position offers strong opportunities for scientific development, international collaboration, and close contact with industry. The PhD candidate will be supervised by Prof. Adil Rasheed at NTNU.
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 : 15th June 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Atomistic Simulations of Rare Events
The ∞RETIS project will enable virtual experiments of important processes within chemistry, physics, and biology, which are currently impossible due to the large timescales that need to be simulated. We will achieve this by implementing a recently developed groundbreaking algorithm in an advanced computer code that can simulate and analyze the dynamics of complex molecular processes, rather than just the thermodynamics. In order to do this, we will integrate path sampling simulation techniques for the study of rare events with a novel approach that allows us to parallelize “non-parallelizable” algorithms by connecting non-synchronous processing unit tasks in a very efficient way. The most advanced path sampling simulations are currently performed by only a handful of research groups in the world due to the required expertise and routinely long (> 1 year) simulation times. Thanks to the the recently developed ∞RETIS algorithm, these simulations can converge within weeks or days depending on the available High-Performance Computing (HPC) resources. In doing so, we can accomplish what was once considered impossible: efficient parallelized computation of time-dependent properties. This proposal aims to leverage this algorithm to tackle simulation systems that were previously inaccessible. This achievement will have a direct impact on the development of chemistry, biotechnology, and material science.
By implementing the ∞RETIS in a user-friendly open-source computer code, we will establish a unique computational tool to reduce the wall time of present-day path simulations 10-fold or more. In addition to this code development, which will be open access and therefore free to use by any researcher in the world, we will deliver compelling interdisciplinary applications in four key areas: i) redox chemical reactions, ii) biomineralization, iii) protein-ligand binding, and iv) enzymatic catalysis.
Deadline : 15th June 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Material Flow Analysis (MFA) for informing Circular Economy and Ecodesign
This PhD position is part of an academic network working on ecodesign, an international research and training initiative. The network aims to advance ecodesign methodologies and manufacturing strategies that enable circular economy practices at industrial scale, in line with European sustainability and resource-efficiency policy objectives.
Circular economy strategies, including ecodesign, are becoming increasingly important not only for environmental reasons, but also for securing access to critical raw materials (CRMs). This is particularly relevant for batteries and electronics, which rely heavily on imported CRMs, creating vulnerabilities in supply chains and posing risks to European industries that are central to the transition toward carbon-neutral mobility. Circular strategies — including reuse, remanufacturing, and recycling — have the potential to improve resource efficiency, reduce emissions and import dependency, and strengthen supply-chain resilience. However, the development of robust domestic and circular supply chains is currently hindered by a range of technological, economic, social, and regulatory barriers. Addressing these challenges requires stronger coordination among companies and regulators to align business opportunities, infrastructure development, and policy frameworks. In this context, a robust understanding of industrial plants and supply chains as interconnected physical systems is essential for identifying inefficiencies across scales and for aligning regulations with sustainable and competitive business strategies.
The objective of this PhD project is to develop a multi-scale framework for improving the resource efficiency of critical materials used in batteries. The candidate will model the stocks and flows of selected critical raw materials within industrial plants, across value chains, and at the EU level using Material Flow Analysis (MFA). Scenarios will be used to identify potential bottlenecks, problem shifts, feedbacks, and delays for different intervention options with the aim to identify opportunities for enhancing resource efficiency and circularity at multiple scales, from individual facilities to broader industrial systems and the European Union. To ensure the scientific robustness and practical relevance of the research for both industry and policy makers, the PhD project includes two mandatory international secondments (2–3 months each).
Deadline : 11th June 2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Fellowship in Foundations and Philosophy of Entanglement
Are you interested in interdisciplinary research at the intersection of philosophy and physics? Then you might be the candidate we are looking for. This PhD fellowship is associated with the interdisciplinary research project “Entanglement Metaphysics without Spacetime” (EMwiS). The aim of metaphysics is to give an account of how the world fundamentally is, and many metaphysical theories tacitly assume the fundamental existence of space and time. EMwiS is motivated by the growing body of work in theoretical physics suggesting that spatiotemporal structure may be derivative rather than fundamental. EMwiS explores whether this calls for a corresponding revision at the level of metaphysics: one in which the foundational role of spacetime in explaining physical order, connectedness, and world structure is replaced by patterns of entanglement, information‑theoretic constraints, or other non‑spatiotemporal structures.
Deadline : 10th June 2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Robust Uncertainty Quantification for Risk-Informed AI Decision Making
The successful applicant will work at NTNU in Trondheim. The Department of Computer Science, NTNU, will host the PhD position. The topic of the PhD fellowship is in probabilistic AI, a fairly new research area at the intersection between modern machine learning and statistics. Specifically, we will investigate methods for well calibrated uncertainty quantification, and their use in decision making. A successful candidate will be offered a three-year position, which could potentially be extended with teaching duties.
The PhD student will be conducting research in the area of probabilistic machine learning and AI-driven decision making under uncertainty. Modern AI systems are increasingly being deployed in high-stakes settings where decisions must be made responsibly and with a clear understanding of what the AI does and does not know. A key challenge in such settings is that AI models are never perfect: they are trained on limited data, may not fully capture the complexity of the real world, and are often applied in conditions that differ from what they were designed for. This research will develop principled methods for quantifying and communicating this uncertainty in a reliable and robust way. Good solutions to this problem have the potential to fundamentally improve how AI systems support decision making in critical applications. When an AI system can accurately express its own confidence, and flag when it is operating outside its area of expertise, decision makers can better manage risk, avoid costly mistakes, and ultimately trust the system’s recommendations. The research will contribute to the foundations of safe AI, with relevance across a broad range of application domains where decisions carry real consequences.
Deadline : 10th June 2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Candidate in Algebraic Topology
A PhD fellowship in algebraic topology and tensor-triangulated geometry, supervised by Drew Heard, is available at the Department of Mathematical Sciences at NTNU starting in the fall of 2026. The successful candidate will be offered a three-year position without teaching; the Department may also offer a six- to twelve-month extension with career enhancing work (i.e. teaching).
Deadline : 10th June 2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD fellow in Metaphysics of Physics
Are you interested in interdisciplinary research at the intersection of metaphysics and physics, then you might be the candidate we are looking for. This PhD position is part of an interdisciplinary research project that explores how recent developments in theoretical physics—particularly in quantum mechanics and quantum gravity—challenge traditional metaphysical assumptions. As a PhD fellow, you will contribute to this research while developing expertise at the boundary between metaphysics and physics in a stimulating research environment.
The aim of metaphysics is to give an account of how the world fundamentally is, and metaphysics is in trouble. The problem is that most metaphysical theories are deeply reliant on the fundamental existence of space and time. What is a world, metaphysics, for instance, asks? One standard answer is that a world includes everything that is related in space and time. The problem is that physicists’ work to combine quantum mechanics and gravity has led to a growing consensus that spacetime is not fundamentally real. Inside black holes, for instance, space and time seem to cease to exist. Is the inside black holes therefore not in the world? In this PhD position, you will help answering questions like this. Thus, if you are a physicist interested in foundational questions or a philosopher interested in the implications that modern physics has for metaphysics, then this PhD position is right for you!
You will be part of a project whose guiding idea that the role that spacetime plays in metaphysical theories can instead be played by quantum entanglement. Entanglement is a strange kind of coordinated behavior between quantum systems – strange because the coordination cannot be explained as prearranged. The hypothesis of the project is that entanglement can be understood as an irreducible, external relation. And the aim is to use it to construct entanglement-based accounts of what a world is, how elements of a world get individuated, and what laws of nature are.
Deadline : 5th June 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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