KTH Royal Institute of Technology, Stockholm, Sweden 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 KTH Royal Institute of Technology, Stockholm, Sweden.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Experimental Fluid Mechanics & Artificial Intelligence
The Department of Engineering Mechanics pursues basic and applied research in biomechanics, composite mechanics, fluid mechanics, contact mechanics, fracture mechanics and fatigue, material mechanics, and paper mechanics. FLOW is a strong community of researchers engaged in fundamental and applied research in fluid mechanics within the department.
We are currently seeking a PhD student in fluid mechanics to join the Fluid Mechanics Division at KTH. This position offers an opportunity to contribute to a research program focused on multi-scale urban turbulence and gust dynamics.
The project aims to uncover the fundamental mechanisms governing fine-scale, intermittent turbulence in urban environments, which is crucial for applications such as drone safety, pedestrian comfort, and urban wind energy. By integrating artificial intelligence with advanced wind tunnel experiments, the PhD student will work to understand and predict urban turbulence patterns. The goal is to develop frameworks that can inform urban planning, renewable energy strategies, and drone operations, addressing critical challenges in sustainable city design.
Deadline : 31.Mar.2026
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Sustainable Metallurgy: Slag Design & Refractories
A project funded by Sweden Vinnova “Refractory Life Extension and Fluorspar Elimination in the AOD Process” (REFLAOD) aims at eliminating the use of fluorspar during stainless steel production thereby enhancing material resilience, prolonging the refractory lifetime, and facilitating higher recycling rates of the stainless steelmaking slags. The PhD candidate will collaborate closely with industry experts to develop a fluorspar-free AOD process, balancing different aspects such as steel quality, refractory wear, and raw material costs. The PhD candidate will make use of thermodynamic tools (e.g. Thermo-Calc) for process design and conduct high-temperature experiments to verify the calculations. The position offers a holistic learning experience in making highly impactful research and also in steering process development, which will foster skills necessary to pursue both an academic and an industrial career after graduation.
Deadline : 26.Mar.2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Turbomachinery and fluid mechanics
Do you want to contribute to Sweden’s capabilities regarding aircraft engines? To increase Sweden’s resilience in aircraft engines, a jet engine demonstrator is being established within the industry to develop components and systems at TRL levels 5-6 for new jet engines. This research project concerns inlet distortions for jet engines and the characterization of distortion patterns. Specifically, air intake distortion is studied in a lab environment, using aerodynamic methods, to further develop a distortion generator and measurement module for characterizing distortion patterns in advanced air intakes. The developed modules will be implemented in a jet engine demonstrator in the industry at a later stage. An overall goal of the project is also to increase national competence in propulsion (jet engine technology).
This research project is in close collaboration with industry and is funded by Vinnova through the national aviation research program, NFFP.
Deadline : 20.Mar.2026
(04) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Nuclear Power Safety
The group of Nuclear Power Safety in Division of Nuclear Science and Engineering (NSE) conducts research on accident phenomena of risk importance to existing and future nuclear reactors, and performs safety analysis for nuclear power plants, including design basis accident analysis and severe accident analysis. The research projects involve extensive international cooperation.
We are looking for a doctoral student to carry out frontier research on melt-structure interactions during severe accidents of light water reactors and perform safety analysis for nuclear power plants. The doctoral student project is supported by the funds from the APRI-12 program and SSM projects.
The successful candidate is expected to (i) perform experimental and analytical studies on melt-structure interactions important to ex-vessel corium risk analysis of light water reactors; (ii) perform safety analyses of nuclear power plants using simulation tools (e.g. U.S. NRC codes).
Deadline : 16.Mar.2026
(05) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Distributed Computing
This Ph.D. project will develop fundamental theory and methods in distributed systems. The research will explore how distributed computing principles can be applied across diverse domains, with potential applications ranging from large-scale graph processing to emerging areas such as edge computing, networked systems, and beyond.
The work will range from theoretical and algorithmic development of distributed protocols and coordination mechanisms, through the design and implementation of distributed frameworks and systems, to experimental evaluation and verification. The Ph.D. student will work in an interdisciplinary environment at the intersection of distributed systems, graph theory, and emerging computing paradigms, with access to high-performance computing clusters and experimental testbeds.
Deadline : 15.Mar.2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Human Computer Interaction for XR in education and industry
The School of Electrical Engineering and Computer Science (EECS) invites applications for a full-time position for a PhD student in human-computer interaction with a focus on extended reality. The main task is to conduct research and development within an EU project (ARISE). Advanced Realities for Cultural Innovation in Europe (ARISE) aims to train the next generation of specialists and innovators in Human–Computer Interaction and Design with a focus on Extended Reality (XR) in Europe. ARISE will achieve this goal by designing and delivering a double-degree master’s programme (ISCED Level 7, 120 ECTS) in Human–Computer Interaction (HCI) and Extended Reality and a minor in Innovation and Entrepreneurship.
We invite highly motivated candidates with an interest in international cooperation, to work for the development of the new Master Program, as well as to develop extended reality systems in a user-centered way to collaborate in our research group and to be trained as an expert in human computer interaction with extended reality. Specifically, the Ph.D. candidate will create diverse XR experiences and evaluate them with users for education and industry.
The student is expected to produce research results with open access to peer-reviewed conferences and journals. The PhD student will also be able to develop project management skills for international projects.
Deadline : 15.Mar.2026
(07) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in the history of polar governance in the national polar research school
We are seeking a PhD student interested in the history and geopolitics of governance in the polar regions. Particularly welcome are proposals that concentrate on Antarctica, non-Western perspectives on polar regions, bi-polar commonalities or languages other than English. Your proposal can embrace any theoretical tradition but needs to fit into one of the three fields covered by the program: the history of the environment, technology, or science. Both marine and terrestrial perspectives are welcome, as well as studies on the role of non-state actors in polar governance.
This doctoral position is a part of the National Research School of Excellence in Arctic and Antarctic Learning (SEAL), which offers PhD students a world-class training environment spanning the natural sciences, technology, law, and humanities, with a strong emphasis on stakeholder engagement and science diplomacy. Within SEAL, you will have opportunities to join field courses with natural science PhD students from Sweden and abroad. A successful candidate must thus be open to interdisciplinary learning and interaction.
Deadline : 12.Mar.2026
(08) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in soil and rock mechanics
We are seeking a doctoral student to join the Division of Soil and Rock Mechanics and contribute to the climate adaptation of society’s infrastructure. This 4-year position is part of a novel interdisciplinary research project focused on developing sustainable solutions for climate adaptation strategies. The project has high practical relevance and is expected to make an important impact on society’s climate transition. As a doctoral student, you will work closely with both geotechnical engineers and risk philosophers to study how uncertainties in climate change predictions influence the reliability of geotechnical structures.
Deadline : 12.Mar.2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in geodetic surveying with GNSS
We are looking for a PhD student for the project Efficiency Improvement of Track
Measurements, funded by the Swedish Transport Administration. The main purpose of the project is to design and evaluate GNSS measurement methods that meet the requirements for determining track position. See more detailed project description here.
The PhD student will be placed at the Department of Land Surveying – Property Science and Geodesy and will collaborate with RISE and the Swedish Transport Administration, which are parties to the project. The candidate is expected to have good knowledge in geodesy (GNSS and reference systems), physics (propagation of electromagnetic signals) and mathematics (statistics).
Supervision: Prof. Milan Horemuz and a person at the Swedish Transport Administration are proposed to supervise the doctoral student. Decision will be made on admission.
Deadline : 12.Mar.2026
(10) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Signal Processing for 6G Communications
Wireless communication technology has evolved over five generations, but the demand for higher efficiency, availability, and reliability is never-ending. The physical layer of 5G and 6G networks revolves around the multi-antenna MIMO technology. 5G uses 64 antennas in each base station and 4 antennas in devices, which might grow by 5-10 times in 6G. These antennas can be used for beamforming, spatial multiplexing, and integrated communication and sensing (ISAC).
In this doctoral project, you will contribute to cutting-edge 6G MIMO technology research and develop new communication theory and signal processing algorithms. The goal will be to develop theory, algorithms, and network architectural concepts to deliver ubiquitous network services across the globe. A hypothesis is that the solution will build on a next-generation cell-free MIMO technology, uniting the ground-based distributed base stations and low-earth-orbit satellites. Methods from MIMO communication theory, array signal processing, optimization, and machine learning will likely play a vital role. The research will mainly be theoretical, but KTH has testbeds for potential experiments. However, we are not looking for microwave engineers because no antennas or hardware will be built.
Deadline :12.Mar.2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in AI and the future of teacher education
LärA-AI is a pioneering graduate school exploring how generative AI (GenAI) transforms the teaching profession, psychosocially, physically, and digitally, amid increasing stress, ethical tensions, and rapid tech change in schools. As AI supports planning, assessment, and decision-making, teachers face shifting roles, added pressures, and new demands for ethical judgment and digital skills.
The graduate school is a collaboration between KTH Royal Institute of Technology, Uppsala University and the University of Gävle – three universities with cutting-edge research in the fields of education, digitalisation, workplace practices and AI. The research school includes nine interdisciplinary doctoral projects across three themes:
- GenAI and the changing nature of pedagogical work,
- Professional autonomy, responsibility, and ethical decision-making, and
- Digital competence, professional learning, and organizational change in AI-integrated schools.
The doctoral position is located in the research group LEaP (Learning, Education and Pedagogy) at the Department of Learning in Engineering Sciences, School of ITM, KTH Royal Institute of Technology.
Deadline : 12.Mar.2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in AI and the future of teacher education
Teacher educators in science and technology (NVTe) plays a pivotal role in preparing skilled NVTe teachers for the Swedish educational system. Yet, NVTe teacher education is under significant pressure from declining student interest in STEM subjects, to a need for robust subject knowledge in teacher training, and growing demand for confidence in rapidly emerging digital pedagogies.
The doctoral student will be working with and contributing to:
- strengthening research capacity within teacher education across eight higher education
institutions, while generating high-quality research in NVTe didactics, - supporting Sweden’s national STEM strategy and AI mandate.
- raising the standard of evidence-based science and technology teaching in Swedish classrooms.
Deadline :12.Mar.2026
(13) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Data-driven AI based Battery Aging Modeling
To support the transition toward smarter and more sustainable building energy systems, the ALTBESS project, funded by the Swedish Energy Agency, focuses on advancing battery energy storage system (BESS) operation in buildings integrated with photovoltaic (PV) systems. The project investigates how data-driven methods and advanced optimization can improve battery performance while explicitly accounting for aging effects. The project is based on real-world case studies and combines simulation, optimization, and operational data.
The doctoral student will develop battery aging models tailored to the project’s case studies using transfer learning and data-driven techniques, integrate the aging models into existing optimization and scheduling frameworks to enable aging-aware battery operation, and consolidate the developed models into a user-friendly software package suitable for researchers and practitioners. This position offers an opportunity to work at the intersection of AI, energy system optimization, and battery technologies in an applied research setting with academic and industrial collaboration.
Deadline :12.Mar.2026
(14) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student (lic) From Behaviour to Emissions: Modeling modal shift
The transition toward sustainable mobility requires both a deep understanding of behavioural dynamics and robust forecasting tools that can anticipate future changes in car ownership and emissions. The overall objective of the research project is to develop a comprehensive framework that integrates behavioural theory, deep learning and xAI, and resilience analysis to understand the impact of infrastructural interventions on modal shift and transport emissions. This is part of a large European project where interventions will be designed and implemented by prioritizing public transport in several municipalities, including mobility and service hubs to promote intermodal transport usage. Main activities include methodological concept development with literature review, data collection and analysis, model validation and stress testing.
Deadline : 12.Mar.2026
(15) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Theoretical Condensed Matter Physics
The group of Egor Babaev at the Department of Physics of KTH Royal Institute of Technology has an opening for one Doctoral student in the field of theoretical condensed matter physics. The group carries a broad spectrum of research using analytical and numerical methods ranging from effective field theory, topological solitons, statistical mechanics of the phase transitions, topological systems to modern microscopic quantum many-body techniques. One of our research topics is the emerging field of composite order: In a recent year, experimental discoveries were made that revised half-century-old paradigms: the discovery of electron quadrupling condensate, and topological defects carrying an arbitrary fraction of magnetic flux quantum. The discovery opened up many fundamental questions on the nature of this new state of matter and its possible applications. Other topics include other new quantum symmetry-breaking and topologicallyordered states, unconventional phase transitions, and application of newly developed methods, such as Diagrammatic Monte-Carlo to understand the long-standing problems. Collaboration with local and international theoretical and experimental groups involved in the project is encouraged.
Deadline : 12.Mar.2026
(16) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in integrated TN-NTN 6G and future networks
We are opening two PhD student positions within two major research initiatives at KTH: SMART-6GSAT and 3D-NET. These projects aim to shape the next generation of wireless networks by developing energy-efficient, resilient, and cost-effective 6G infrastructures that integrate terrestrial and non-terrestrial segments and support advanced digital services. Students will be part of a large international collaboration with leading academic, industry, and public partners, working on research questions that are both fundamental and directly relevant to the design of future networks.
Within SMART-6GSAT, one PhD positions is available. The focus is on exploring terrestrial–non-terrestrial (TN–NTN) integration and network design, with emphasis on architectures and protocols that enable seamless connectivity across ground, aerial, and satellite domains, with energy efficiency by design. The 2nd position, within 3D-NET, will study AI-assisted network control and resource management for integrated terrestrial, aerial, and satellite networks, focusing on joint optimization of radio, cloud, and network resources to enable resilient, energy-efficient 6G connectivity. Together, these positions provide unique opportunities to advance both theory and applied technologies in 6G, while contributing to standardization, demonstrators, and Europe’s leadership in future connectivity.
Deadline : 11.Mar.2026
(17) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in technology foresight and digital skills forecasting
We are seeking a doctoral student at the Department of Industrial Economics and Management (INDEK), KTH Royal Institute of Technology, with a focus on technology foresight and forecasting future skill needs in industrial companies undergoing advanced digitalisation, particularly in relation to AI, automation, and robotics.
The PhD project aims to develop and apply quantitative and hybrid forecasting methods to analyse how tasks, roles, and skill requirements evolve in environments where work is increasingly hybridised between humans, AI systems, and automated technologies (AI-augmented work). A central component of the project is method development, combining statistical, machine-learning-based, and data-intensive approaches with qualitative and participatory foresight methods such as scenario planning and expert elicitation. The field is developing rapidly, and the position constitutes a key role in contributing to its advancement.
The work will be conducted in close collaboration with industry partners within projects funded by Vinnova’s programmes for advanced digitalisation, involving extensive industry interaction and access to empirical data from real-world industrial settings. Collaboration with internationally leading academic environments is also included.
Deadline : 11.Mar.2026
(18) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Optimal Transport for Optimization and Machine Learning
The project focuses on applying optimal transport and gradient flows to machine learning and optimization applications, such as deep generative models, sampling, inference, stochastic optimization, and beyond. The doctoral student will develop and analyze mathematical models and algorithms that connect partial (and/or stochastic) differential equations, infinite-dimensional optimization, and statistical machine learning. The goal is to build a theoretical and computational foundation for new methods in statistical inference and generative modeling, based on principled optimal transport and gradient flow theory.
We are looking for a highly motivated candidate with a strong background in mathematics, applied mathematics, or related fields, and with an interest in both theory and applications.
The doctoral student will be placed at the Department of Mathematics, School of Engineering Sciences (SCI), KTH Royal Institute of Technology, and will be part of a dynamic research environment with close collaboration with other researchers in the area.
Deadline :09.Mar.2026
(19) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Generative Modeling, Super Resolution of Brain Imaging
This project involves generative modeling to address missingness in mass spectrometry imaging (MSI) of brain tissue. The missingness can happen along two dimensions: spatial (super resolution) and feature (data imputation). Enhancing the quality of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching.
Therefore, the doctoral student is primarily expected to be comfortable with using and developing new machine learning models, specifically generative ones, and publishing the results at top machine learning venues. The project involves collecting and curating a MSI benchmark in the first year targeted for NeurIPS 2027 to be then used as the test-bed for the developed ML techniques in subsequent publications. The student will join Azizpour’s group at KTH. The position is in collaboration with the CBH school, and Uppsala University. It is funded by the the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and the student joins its research program.
Deadline : 07.Mar.2026
(20) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Generative Modeling of Biomolecules
Cryo-EM is a method for reconstructing the 3D structure of a biological molecule using images from electron microscopes. However, the images obtained from the electron microscope are often plagued by high levels of noise, which is typically mitigated by using a large number of images, which in turn leads to high data collection costs.
This project proposes a new method for reconstructing 3D structures of molecules from cryo-EM images using generative models. The prospective student will join Azizpour’s group and develop novel generative models of biomolecules’ structures to be used, by other project members, as a prior in a Bayesian inference of the biomolecules’ dynamics in order to construct more reliable 3D structures. The results are expected to be published at top machine learning venues such as NeurIPS, ICML, and ICLR.
The project is a collaboration within Digital Futures between the Departments of Intelligent Systems and Mathematics at KTH and the Department of Biochemistry and Biophysics at SU.
Deadline : 07.Mar.2026
(21) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student polymeric materials in nuclear power plants
We are building a long-term research environment in nuclear technology at KTH to deliver safer life extension estimations of existing Swedish nuclear power plants. The environment integrates three pillars: (i) fundamental understanding of ageing mechanisms in safety-critical polymer components under service-relevant thermal, radiative, and environmental conditions, including mitigation strategies; (ii) non-destructive condition monitoring based on portable, single-sided low-field T2-NMR adapted for plant use; and (iii) uncertainty-aware AI models embedded in digital twin workflows that fuse laboratory and online plant measurements to predict remaining useful life (RUL) and support risk-based inspection, maintenance, and replacement.
The purpose of this subproject, as part of the research environment in nuclear technology, is to focus on the mechanisms and kinetics of ageing of polymeric components in nuclear power plants (NPPs), which will enable a higher precision in life-time estimation of components used to day, and a basis for the design of new materials with extended lifetime in future installations. Examples of research questions are: How do temperature, irradiation, humidity, dose rate and oxygen availability interact to drive chemical/mobility changes in cables, seals and coatings?Under what conditions do DLO and plasticiser migration dominate, and how can acceleration protocols remain representative?
Deadline : 05.Mar.2026
(22) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in material physics and non-destructive diagnostics of polymers
The project focuses on the development of non-destructive methods for condition monitoring and lifetime assessment of polymeric materials in nuclear environments. The work is part of a larger, interdisciplinary research programme funded by the Swedish Research Council (VR), aiming to improve the safety and lifetime of polymeric components used in nuclear power, such as cables, seals, and protective materials.
The doctoral project combines low-field NMR with advanced material characterization to establish quantitative relationships between molecular dynamics, microstructure, and functional degradation in polymeric materials. Particular emphasis is placed on how fillers, stabilization strategies, and active molecules influence local segmental mobility, diffusion, and NMR relaxation, and how these relationships evolve under controlled thermal ageing.
The objective is to develop material-anchored condition indicators that enable robust, non-destructive lifetime assessment in safety-critical environments. The project is carried out in close collaboration with other work packages within the programme, including material development and AI-based modelling, and provides the doctoral student with access to a strong research environment with modern infrastructure and international collaborations.
Deadline : 05.Mar.2026
(23) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in models, algorithms, and optimization for machine learning
We invite applications from talented and highly motivated candidates to pursue a PhD in machine learning at KTH, Sweden, and NTU, Singapore. This is a fully funded, joint doctoral position that will lead to a joint PhD degree awarded by KTH and NTU. The successful candidate will be supervised by professor Aristides Gionis (KTH), associate professor Kelly Ke Yiping (NTU), and assistant professor Sebastian Dalleiger (KTH). The doctoral student will be recruited and formally enrolled at KTH, and will be required to spend a minimum of 12 months at NTU in Singapore as part of the joint program.
The research project is broadly situated in the field of machine learning. Potential research topics include, but are not limited to, algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine learning, representation learning, and fair, accountable, and transparent machine learning.
Applicants must hold a Master of Science degree by the time of enrollment. Successful candidates should be highly self-motivated and committed to publishing and presenting high-quality research. Solid background in algorithms design, machine learning, and optimization is essential, along with strong programming and implementation skills.
Deadline : 05.Mar.2026
(24) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Machine Learning
PhD student 1 will be a part of an EU project “Digitising Smell: From Natural Statistics of Olfactory Perceptual Space to Digital Transmission of Odors”. The goal of the project is to digitalize the sense of smell, pave the ground for understanding how the sense of smell works in humans and build AI models of these. We seek a candidate who have experience working with time series data (EEG), signal processing and machine learning.
PhD student 2 will be a part of an EU project “Digitising Smell: From Natural Statistics of Olfactory Perceptual Space to Digital Transmission of Odors”. The goal of the project is to digitalize the sense of smell, pave the ground for understanding how the sense of smell works in humans and build AI models of these. We seek a candidate who have experience working with computer vision, machine learning and video processing.
Deadline : 05.Mar.2026
(25) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Robot Learning for Manipulation
PhD students will work on robot learning for manipulation, exploring recent advances in Vision-Language-Action (VLA) models for tabletop manipulation tasks with a focus on using the whole-body motion to enhance perception and control for solving complex mobile manipulation tasks. We are seeking candidates with a strong background in robotics and machine learning, and demonstrated experience in two or more of the following areas: deep learning, reinforcement learning, robot perception, navigation or manipulation.
Deadline : 05.Mar.2026
(26) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Material Science
The department of materials science and engineering is looking for a PhD student in materials characterization and developing structure-property correlation in advanced high-strength steel (AHSS) with a focus on fatigue performance of thick-plate AHSS for the automotive industry. With the growing demand for sustainable material solutions to reduce carbon emissions in the automotive industry, AHSSs have emerged as the most cost-effective option for achieving lightweight designs, particularly in heavy-duty vehicles. Given that structural components in such vehicles are predominantly subjected to dynamic loading, fatigue performance becomes a critical factor in material and component design. This is particularly important for chassis components, where shearing processes such as hole punching and trimming—essential steps in manufacturing—significantly influence the mechanical properties of the steel and thereby the chassis’ lifetime and performance. Despite their importance, the relationship between microstructural constituents and the mechanisms of fatigue crack initiation and propagation in these materials remains underexplored. The overarching goal of this project is therefore to develop fatigue-optimized AHSS solutions tailored for dynamic automotive components. This involves an in-depth understanding of microstructures and mechanical properties to be able to design the materials to meet the simultaneous requirements of high static strength and enhanced fatigue performance. The project will be conducted within the center for mechanics and materials design (MMD) and Steel4Fatigue project, which involves collaboration of academic and industrial partners.
Deadline : 05.Mar.2026
(27) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student (Licentiate) in Engineering Materials Science
Cemented carbide tools are essential to Sweden’s manufacturing, construction, and mining industries due to their exceptional hardness and wear resistance. At the same time, the supply of virgin tungsten (W) and cobalt (Co) is uncertain, and only about 10% of today’s cemented carbide tools are produced from recycled material. Increasing recycling is therefore crucial for reducing energy use, climate impact, and dependence on critical raw materials.
The aim of the PhD project is to contribute to a mechanistic understanding of how recycled raw materials influence the microstructure and performance of cemented carbides in demanding applications, primarily metal cutting but also rock excavation. Recycled raw material often contains impurities which can affect the final properties of the product. The work builds on a pre-study in which real‑time characterization under simulated loading conditions showed strong potential for explaining how impurities and microstructure govern material behaviour.
The project will be carried out within the CHARM project funded by Vinnova and involves close collaboration with leading industrial partners (Sandvik Coromant, Seco Tools, Sandvik Mining) as well as researchers at KTH and Lund University. It is also linked to Vinnova’s competence centre NEXT and European networks such as AIM‑NEXT and RESQTOOL. The work includes development of new characterization methods, analysis of microstructure–property relationships, and knowledge‑based optimization of recycled cemented carbides.
Deadline : 05.Mar.2026
(28) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in machine learning
We invite applications from talented and highly motivated candidates to pursue a PhD degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/). The research team focuses on developing novel methods to extract knowledge from data, modelling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but are not limited to models and algorithms for knowledge discovery, novel algorithmic and statistical techniques for big data management, optimization for machine learning, analysis of information and social networks, fairness, accountability, and transparency in learning systems.
Applicants must hold a Master of Science degree by the time of enrollment. Successful candidates should be highly self-motivated and committed to publishing and presenting high-quality research. Solid background in algorithms design, machine learning, and optimization is essential, along with strong programming and implementation skills.
The advertised position is funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.
Deadline :05.Mar.2026
(29) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Application-Level Synthesis for Electronic Systems Design
KTH is looking for a PhD candidate to develop a robust Application-Level Synthesis (ALS) tool to compose an application from pre-synthesised Algorithm Implementations using High-level Synthesis. The key differentiation of this ALS tool will be its target of a Coarse Grain Reconfigurable fabric based on Silicon Lego (SiLago) bricks. SiLago enables the calculation of physical design-aware cost metrics during its design space exploration. The SiLago framework is part of many EU, IMEC, and national projects and is being used for real-life industrial applications and also for next-generation CMOS technologies.
Deadline : 05.Mar.2026
(30) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Learning and Control of Networked CPHS
The Division of Decision and Control Systems is looking for up to two doctoral students with a strong background in control systems, mathematics, and modeling, and with a keen interest in network theory.
The project lies in the intersection of network dynamics, learning, and control, and aims at addressing theoretical challenges within networked cyber-physical-human systems (cphs). These systems are characterized by humans in interconnected communities making decisions while interacting with a cyber-physical or control system, and applications include complex socio-technical systems such as smart cities. The project will include learning complex dynamics from big data and rigorous characterization and (data-based) modeling of decision-making dynamics over networked cphs, in addition to the development of novel tools to design and assess the impact of control strategies or interventions.
The Division of Decision and Control Systems conducts, among others, fundamental research in networked control systems. The PhD project will include potential collaborations with the KTH Live-in Lab and involvement in experimental design. Moreover, given the natural interdisciplinary aspect of the proposed network perspective for cphs, collaborations with scientists from other disciplines will be encouraged.
Deadline :05.Mar.2026
(31) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in nuclear materials
We are seeking a PhD student to work on the NUCLEAR project (NUclear Component Laboratory for Engineering Advancement and Research) within the research platform NuMaP (Nuclear Materials Platform) (numap.se). The project aims to develop the experimental laboratory environment as well as to conduct modeling and experimental work on advanced nuclear materials. NuMaP brings together KTH Royal Institute of Technology, Luleå University of Technology, Uppsala University, Linköping University, and Chalmers University of Technology with the nuclear industry in Sweden, and is funded by the Swedish Energy Agency. The PhD student will be part of a multidisciplinary team of researchers and students.
The doctoral student will participate in several sub-projects within NuMaP, with a primary focus on additive manufacturing of advanced nuclear materials such as alumina-forming steels and nuclear fuel, as well as on modeling and measurement of transport properties in these materials.
Deadline : 04.Mar.2026
About KTH Royal Institute of Technology, Stockholm, Sweden – Official Website
KTH Royal Institute of Technology, abbreviated KTH, is a public research university in Stockholm, Sweden. KTH conducts research and education within engineering and technology, and is Sweden’s largest technical university. Currently, KTH consists of five schools with four campuses in and around Stockholm.
KTH was established in 1827 as Teknologiska Institutet (Institute of Technology), and had its roots in Mekaniska skolan (School of Mechanics) that was established in 1798 in Stockholm. But the origin of KTH dates back to the predecessor to Mekaniska skolan, the Laboratorium Mechanicum, which was established in 1697 by Swedish scientist and innovator Christopher Polhem. Laboratorium Mechanicum combined education technology, a laboratory and an exhibition space for innovations. In 1877 KTH received its current name, Kungliga Tekniska högskolan (KTH Royal Institute of Technology). The King of Sweden Carl XVI Gustaf is the High Protector of KTH.
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