Uppsala University, 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 Uppsala University, Sweden.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral (Phd) position in Applied Mechanics in Multiphysics Modeling of Rapidly Deforming Structures in Harsh Environments
The sky-rocketing prevalence of impact-induced bone fractures, driven by an aging population and a growth in traumatic incidents such as traffic collisions and critical falling, is leading to a higher demand for bone repair methods. Biodegradable metal alloy 3D-printed lattice scaffolds are aimed to sustain mechanical loads and at the same time stimulating bone growth into the lattice while the lattice itself degrades and eventually the injured location is completely replaced by newly formed bone.
With financial support of the Swedish Research Council, the research project focuses on the development and implementation of high-performance numerical algorithms for multiphysics simulation of impulse-initiated fractures in porous structures using phase-field models. Phase-field theories are beneficial due to their flexibility in capturing coupled physical phenomena across multiple length and time scales. The developed algorithms will primarily be implemented on computer clusters to simulate porous and/or randomly structured heterogeneous materials soaked in fluids with the aim of designing implants for the treatment of severe bone fractures and tissue regeneration. A natural part of the project is to conduct sophisticated experiments using ultra-high-speed MHz X-ray in-situ imaging of bone/lattice systems soaked in fluid and subject to impulse loads at the ESRF synchrotron to support the computer simulations.
A long-term goal is to provide useful numerical engineering tools to Swedish industry for an optimal design of load-bearing structures with complex geometries in harsh environments.
Deadline : 30 April 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD position in evolutionary genomics
Would you like to conduct research on the evolution of sex chromosomes and sex ratio, supported by skilled and friendly colleagues in an international environment? Are you looking for an employer committed to sustainable working conditions and offering secure and advantageous terms of employment? You are welcome to apply for a PhD position at the Department of Ecology and Genetics at Uppsala University!
The Department of Ecology and Genetics (IEG) is part of the Evolutionary Biology Centre (EBC), a vibrant and internationally recognized hub for evolutionary and ecological research. At IEG, we work at all levels of biological organization (from molecules to ecosystems) and study systems (plants, animals, fungi, bacteria, etc.). The Program for Plant Ecology and Evolution, where this position is located, is home to eight research group focusing on genomics, adaptation and speciation as well on community ecology. Researchers have access to excellent glasshouse and climate-controlled facilities, fully state-of-the-art molecular labs and a high-performance computing cluster (UPPMAX). The Department works closely with the national infrastructure facility SciLifeLab.
Our PhD students are part of the Biology PhD School that brings together students from multiple departments and organizes symposia, courses, newsletters, and science meetings for PhD students. We value collegial support and work actively to prepare our PhD students for a continued career in academia or elsewhere.
This position offers an exciting opportunity to develop a broad and modern skillset while contributing to new insights in evolutionary genomics.
Deadline : 30 April 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral (Phd) position in Applied Mechanics in Multiphysics Modeling of Dendrite Growth in Metal-Based Batteries
The project concern theoretical method development for multiphysics modelling of battery materials. Metal battery electrodes offer exceptional advantages in terms of energy density, cost-efficiency, and sustainability, and they also enable the exploration of novel, environmentally friendly batteries. Despite recent advances, the fundamental complexity of metal electrode behavior continues to hinder progress. A major issue is the formation of dendrites, i.e., needle-like metal nanostructures that emerge during metal plating. These structures pose a safety risk by potentially piercing the separator between the anode and cathode, causing internal short circuits or even thermal runaway. Understanding and controlling dendrite growth is therefore essential, yet the process is inherently complex and dynamic, governed by a multitude of physicochemical and mechanical parameters.
Specifically, high-fidelity numerical algorithms based on phase-field multiphysics theories will be developed and implemented on computer clusters to simulate dendrite formation. Phase-field models are beneficial owing to their flexibility in capturing coupled physical phenomena across multiple length and time scales. The project focuses on the development and refinement of the physical models used in numerical simulation of electroplating of metals by addressing the charge transfer kinetics and the contribution of mechanical deformation, i.e., mechanical stress effects induced by material phase changes/swelling during dendrite growth. The derived numerical models have the potential to create a digital twin of the electrochemical cell that enables materials innovation for next-generation battery technologies.
The project is a collaborative effort between researchers in computational mechanics and solid-state chemistry and is part of COMPEL (Competence and excellence for the electrification of the transport system), a strategic initiative from the Swedish government to ensure Sweden’s long-term competitiveness in battery development and electrification of the transport sector.
Deadline : 30 April 2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD student in astronomy and astrophysics, with focus on gravitationally lensed stars observed with the James Webb Space Telescope
The successful candidate will work on data collected by the James Webb Space Telescope on gravitationally lensed stars at cosmological distances, on the development of new analysis tools, and on the numerical simulation of observational biases in this field.
The doctoral student position is a 4-year appointment, and the candidate will primarily devote the time to their own research studies. Extension, up to a maximum total employment of five years, is possible by including department duties at a level of at most 20%, typically teaching. The position is fully financed and the salary is in accordance with local guidelines at Uppsala University.
Deadline : 30 April 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD student in data-driven precision medicine
This is a four-year pre-clinical doctoral position focused on the analysis and interpretation of high-throughput sequencing data to identify molecular drivers of cancer resistance and relapse, as well as to prioritize therapeutic targets for aggressive solid tumors.
The role involves working with both publicly available and internally generated datasets, with a particular focus on single-cell and bulk RNA sequencing. Responsibilities include independently performing data processing, analysis, and integration to drive assigned research projects. In addition, the position is expected to provide biostatistical support for other ongoing projects within the research group, including statistical analysis and data interpretation. The successful candidate is also expected to be highly motivated and willing to contribute to mentoring students at the undergraduate and master’s levels.
This position requires close collaboration with the National Genomics Infrastructure, operating within SciLifeLab, and is fully on-site with no option for remote work.
Deadline :13 May 2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD in Evolutionary Ecology and genomics
How microbes influence the ecology and evolution of host organisms is a fundamental question in biology. Microbiome can have a strong impact on behavior, health, and immune responses of the host. In response to changing temperatures, the health status of host populations can be influenced by host microbiomes. Broad-scale studies have shown that skin microbiome composition of amphibians changes along latitudinal gradients, and microbiome composition can be disrupted by temperature and infections. However, large-scale studies examining microbiome interactions in wild amphibians remain scarce at Swedish and global scales.
The phD candidate will work on the project “Amphibian skin microbiomes and infection diseases”, within the group of Maria Cortazar Chinarro at the Department of Ecology and Genetics. The position is fully funded for four years. More information about her research can be found here: https://www.uu.se/institution/ekologi-och-genetik/forskning/zooekologi/cortazar-chinarros-grupp
This Phd project aims to explore skin microbiome diversity and composition across populations and at regional scales and broad-scale latitudinal gradients. High-quality metagenomic resources from the skin of both common and endangered amphibian species will be generated to infer phylogenetic relationships and examine how host evolutionary history, genetic variation, environmental (abiotic) factors, and infectious diseases shape host–skin microbiome interactions. To investigate these relationships, the project will characterize the skin microbiome composition—including bacteria, viruses, fungi, and protists—using an integrative approach that combines metabarcoding, metagenomics, and population genomics methods.
Throughout the doctoral programme, the student will apply a wide range of bioinformatic methods and sequencing approaches, including amplicon sequencing analyses, shotgun metagenomics, and metatranscriptomics. The project will also involve extensive fieldwork and molecular laboratory work to generate high-quality sequencing data.
Deadline : 8 May 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD-student in Evolutionary and Ecological Genetics
A central question in evolutionary biology is whether evolution is repeatable, and whether the same genes contribute to repeated phenotypic adaptations. This question has important implications for predicting genetic responses to changing environments. Recent studies show that while phenotypic evolution is often repeatable, the underlying genes involved can be less predictable. This poses a challenge for using genomic data to predict a species’ evolutionary potential. This project will address this challenge by integrating mechanistic insights about phenotypic adaptation with genomic data to understand how species evolve adaptations to changes in their environment.
The PhD candidate will work with the main supervisor David Berger and with the co-supervisor, SciLifeLab fellow Dr. Gabriela Montejo-Kovacevich, both at IEG. The project will leverage data from a large-scale Evolve-and-Resequence experiment in a cosmopolitan insect pest, the seed beetle Callosobruchus maculatus. Replicated genetic lines have undergone experimental evolution under four different temperature regimes for more than 150 generations. This unique system allows us to study the roles of determinism and chance in the evolution of thermal tolerance and niche breadth. Extensive phenotypic, RNA-seq, and DNA pool-seq data are available from multiple time points in the experiment, which is still ongoing. The Berger lab maintains several other C. maculatus strains sampled across its global distribution and closely related seed beetle species. This provides opportunities for comparative genomics and additional experiments and data collection if the candidate wishes to further develop the project.
The PhD project will involve a combination of experimental and molecular laboratory work and bioinformatic analysis, with emphasis on the latter. The exact balance between tasks may depend on the candidate’s interests and the project’s development.
The successful candidate is expected to collaborate with other researchers in the Berger and Montejo-Kovacevich groups, as well as researchers from two other groups working on seed beetles at IEG.
PhD students at the Institute of Ecology and Genetics (IEG) have the option to engage in teaching and to develop their pedagogic CV by taking courses. The type and level of teaching will depend on availability and the student’s interests but cannot exceed 20% full-time employment. Teaching responsibilities will primarily involve assisting in undergraduate courses within the biology section. The position and funding is extended by the time the student has engaged in teaching.
We anticipate the candidate to actively participate in the everyday activities of the research group (in-person meetings with PI, group meetings, training) and at the Program/Department (joint seminars,
Deadline :22 May 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Data-driven precision medicine and diagnostics
The Department of Immunology, Genetics and Pathology at Uppsala University has a broad research profile with strong research groups focused on cancer, autoimmune and genetic diseases. A fundamental idea at the department is to stimulate translational research and thereby closer interactions between medical research and health care. Research is presently conducted in the following areas: medical and clinical genetics, clinical immunology, pathology, neuro biology, neuro-oncology, vascular biology, radiation science and molecular tools. Department activities are also integrated with the units for Oncology, Clinical Genetics, Clinical Immunology, Clinical Pathology, and Hospital Physics at Akademiska sjukhuset, Uppsala. The department has teaching assignments in several education programmes, including Master Programmes, at the Faculty of Medicine, and at the Disciplinary Domain of Science and Technology. The department has a yearly turnover of around SEK 500 million, out of which more than half is made up of external funding.
Deadline : 25 May 2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Computerised Image Processing with focus on Machine Learning for Data-Driven Precision Medicine and Diagnostics
The development of artificial intelligence (AI) and computerised image processing in combination with advanced digital microscopy is enabling major advances in clinical pathology and cancer diagnostics. Today’s AI methods require large amounts of data with a detailed ground truth annotation that the AI system can learn from. In healthcare, in most cases, there is only access to information at the patient level, about the patient’s health status and disease development. In this project, we will develop theory, algorithms and methods to effectively train AI models based on limited and imprecise information as well as unbalanced and heterogeneous multimodal data.
This needs-driven method development finds direct application in healthcare. Together with our partners in healthcare and biomedicine, we will apply the developed methods to detect cancer from cell and tissue samples as early as possible and to individually predict which treatment is expected to give the best result for the patient.
Deadline :22 May 2026
(10) PhD Degree – Fully Funded
PhD position summary/title: Ph.D. Student in Industrial Engineering and Management with a focus on Multimodal AI and XR for Human-Centric Manufacturing
You will conduct research within projects focusing on multimodal AI and XR for human-centric manufacturing. The overall aim is to develop methods and digital solutions that support operators in manufacturing through context-aware guidance, knowledge transfer, skill development, and cognitive and physical support. Your research will focus on developing AI-based methods for analysing and modelling human tasks, workflows, and operator context using multimodal data from manufacturing environments and XR devices (video, audio, motion, gaze, and other sensor streams). This may include, for example:
- developing multimodal AI pipelines for interpreting operator actions, task progress, and work context
- modelling workflows, procedures, and expert knowledge for context-aware support
- contributing to real-time support systems that combine AI, sensors, context models, and XR interfaces
- evaluating usability, trust, acceptance, and human-centered aspects of AI- and XR-based systems in industrial environments
You will work closely with other academic and industrial partners to access data, validate solutions in real environments, and ensure the applicability of the developed methods.
The main task of a doctoral student is to devote to the doctoral education, which includes both participation in research projects and doctoral education courses. The position may include up to 20% teaching and administrative duties.
Deadline : 20 May 2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Physical Chemistry
The PhD student will be part of Physical Chemistry research group at the Department of Chemistry – Ångström and will work in a project focusing on the development of a new time-resolved operando Soft X-ray spectroscopic method for mechanistic studies of electrocatalytic reactions.
The ultimate goal of the project is to apply this new experimental tool to probe the fundaments of electrocatalysis. The doctoral project will involve the conceptual and practical development of the methodology for electrochemically initiated time-resolved soft X-ray spectroscopy, including construction and optimization of measurement setups and electrochemical cells compatible with synchrotron-based spectroscopy. The work also includes preparation and characterization of electrocatalytic materials, performance of electrochemical and spectroscopic experiments, participation in synchrotron measurement sessions, and analysis and interpretation of experimental data.
The project combines physical chemistry, electrochemistry, spectroscopy, and instrumentation development. The PhD student is also expected to contribute to dissemination of results through scientific publications and presentations at international conferences, and to take courses corresponding to third-cycle education.
Deadline : 29 May 2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in cybersecurity for electrical systems
Cyberattacks and cyberwarfare are a growing threat to all critical infrastructure. The electricity system is particularly vulnerable because other critical infrastructure, such as healthcare, communications, and payment systems, depend on it.
We are looking for two PhD students who want to conduct important and challenging research in the area of cybersecurity for power systems. Using artificial intelligence (AI)/machine learning (ML), threat modeling and simulations, we can learn more about how cybersecurity can be improved.
The following are examples of possible projects:
- Cybersecurity for power grid stations and their data communications
- Cyber threats to the electricity system of the future
- Cyber resilience in power systems
- Cybersecure electrical systems using AI/ML
We offer a varied and exciting work that is designed by the doctoral students and the research group together. Each doctoral student will be supervised by at least two supervisors. The Department of Electrical Engineering also offers salary supplements when employed as a doctoral student at the department.
Deadline :May 19, 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Student in Therapy and Diagnostics of Neurodegenerative Diseases
The objective of the PhD project is to generate and evaluate brain‑penetrating antibodies that bind to proteins involved in the onset and progression of neurodegenerative diseases. These antibodies will then be used to develop novel diagnostic tools, such as PET and SPECT radioligands, or for therapeutic applications to investigate whether the antibodies affect pathology in models of neurodegenerative disease. The work will therefore include both in vitro studies, primarily involving immunological and histological methods as well as cell culture, and in vivo studies in models of neurodegenerative diseases.
Doctoral students are expected to devote the majority of their time to their own doctoral education. Other duties at the department, such as teaching and administrative work, may be included within the scope of the position (maximum 20%).
Deadline : 1 June 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD student in computerized image processing and physics-informed machine learning for green hydrogen production
Proton exchange membrane water electrolyzers (PEMWE) are a cornerstone technology for green hydrogen production, but their widespread adoption is held back by cost and durability constraints. A novel strategy for addressing these constraints is the use of thermally sprayed titanium layers for corrosion protection, an approach that enables more cost-effective materials while preserving performance. The challenge is that these layers have a complex, three-dimensional microstructure, and understanding how that microstructure influences electrochemical performance requires methods that can bridge the scales from 3D imaging to functional prediction to process optimization.
The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks:
- 3D microstructure characterisation. The student will develop automated pipelines for segmenting and analysing X-ray computed tomography (XCT) images of titanium layers. This includes deep learning-based image analysis and the extraction of quantitative microstructural descriptors that are physically meaningful and predictive.
- Probabilistic surrogate modelling and digital twin construction. The extracted microstructural descriptors will be used to learn a probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify and propagate uncertainty from image features to predicted PEMWE behavior.
- Bayesian experimental design and process optimization. The digital twin will form the basis for Bayesian optimization of manufacturing process parameters. Rather than running expensive physical experiments at random, the goal is to design experiment sequences that maximally reduce uncertainty about which process conditions lead to optimal performance
The PhD student will be supervised by Ida-Maria Sintorn, Professor in digital image processing, and Jens Sjölund, Assistant Professor in AI at the Department of Information Technology, and conducted in close collaboration with Alleima and Sandvik.
Deadline : 7 May 2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD student in data-driven battery modelling
As the world accelerates toward sustainable electrification, advanced energy storage is more critical than ever. Yet, the battery research powering this transition faces a major bottleneck: data integration. While cell manufacturing and advanced characterizations (like X-ray diffraction, imaging, and tomography) generate massive, complex datasets, this information is often limited to statistical analysis. Meanwhile, robust, physics-based battery models remain isolated from these modern data streams.
This PhD project tackles this challenge head-on by bridging data-driven approaches with physics-based modeling. Your mission will be to develop advanced computational methods that unite prior physical knowledge with large-scale manufacturing and experimental datasets. By successfully linking rich data streams, featured in synchrotron characterization and electrode manufacturing processes, to highly predictive and interpretable models, your work will help redefine how battery materials and processes are optimized.
The project is part of COMPEL. COMPEL (COMPetence for the ELectrification of the transport system) is a strategic initiative by the Swedish government, aimed at strengthening research and educational environments within battery technology and electrification. Within COMPEL, the three institutions – Chalmers University of Technology, Lund University, and Uppsala University – in consultation with Swedish industry, deepen their collaboration with the aim of strengthening Sweden’s position in battery technology contributing to the electrification of the transport sector, and related areas. The three institutions aim to enhance research and education in the battery sector to ensure strong research in battery technology supporting that electrification of the transport sector is maintained and further developed in Sweden. Research within COMPEL spans the entire battery value chain: materials research, research on new cell concepts and cell manufacturing, battery systems and system integration, recycling, and safety.
At Uppsala University within the Department of Information Technology, COMPEL is a strategic initiative aimed at enhancing research environments in battery technology and electrification, in a manner that complements relevant research at other institutions and builds on our strengths.
Deadline : 27 May 2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Structure–Property Relationships in Halide Solid Ionic Conductors
The project aims to understand how crystal structure and crystallinity govern ionic transport in lithium- and sodium-containing halide-based solid electrolytes. The central goal is to establish fundamental structure–property relationships that can guide the rational design of improved solid ionic conductors for next-generation solid-state energy storage applications.
Key duties of this solid-state chemistry work involve planning and performing air- and moisture-sensitive syntheses, determining the average and local crystal structure of the materials, ionic transport characterization, and interpretation of experimental data. The candidate is also expected to contribute to the development of new research directions, including future exploration of mixed-anion compositions.
Deadline :15 May 2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Signal Processing with a Focus on Collaborative Continual Machine Learning
Modern systems increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal processing methods, are typically designed for operation in static environments: a model is trained on a fixed dataset and subsequently deployed for inference. However, real-world environments are often dynamic and non-stationary. As a result, the performance of models trained offline tends to deteriorate when exposed to new signal conditions, previously unseen patterns, or changing system characteristics.
This project aims to address these challenges through the development of adaptive learning frameworks for non-stationary environments. In particular, we focus on continual learning, where models are updated as new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting previously acquired knowledge.
We will study collaborative learning scenarios, where multiple devices or sensors jointly process and learn from data streams. Such settings introduce additional challenges, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims to develop learning methods for such collaborative continual learning scenarios. The resulting methods will be analyzed theoretically and validated on representative datasets, with an emphasis on generalization and scalability.
We have an exciting work environment designed by the doctoral student and the research team together. The doctoral student will be supervised by at least two supervisors. The Department of Electrical Engineering also offers a salary supplement in accordance with the local guidelines for doctoral students at Uppsala University.
Deadline :8 May 2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Systems Theory and Machine Learning
The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics. Optimal transport is a key mathematical concept that allows us to understand notions like inference and sampling as dynamic processes of probability distributions. Building on the theoretical insights, we will develop new methods for machine learning and dynamical systems, including generative modeling and system identification, with applications in biomedical modeling, large-scale autonomous systems, computer vision, and AI for science. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor.
The student will be part of the WASP graduate school. Wallenberg AI, Autonomous Systems and Software Program (WASP) is 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 society and industry. Read more: https://wasp-sweden.org/
The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry.
Deadline :19 May 2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Machine Learning, focusing on robustness in statistical learning theory
In this project, the selected candidate will join us in conducting research in statistical learning, developing data-driven methods to learn models of large-scale signals and systems from data. There will be a strong focus on developing robust methods with mathematical guarantees, focusing on statistical and optimization properties with potential applications to cardiology and medicine. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor. The student will be part of the WASP graduate school.
The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/
Wallenberg AI, Autonomous Systems and Software Program (WASP) is 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 society and industry. Read more: https://wasp-sweden.org/
Deadline :19 May 2026
(20) PhD Degree – Fully Funded
PhD position summary/title: Two PhD positions in the area of Pharmacometrics – Tropical Pharmacology/Global Health
Two doctoral positions are available within the research area of pharmacometrics focusing on global health at the Department of Pharmacy.
The first position is situated within the research group Tropical Pharmacology and Therapeutics. The project is focused on designing and optimizing preventive mass drug administration strategies and treatments for various poverty-related tropical parasitic diseases, such as leishmaniasis and malaria. The position is partially embedded in the ERC-funded project TRANSPHORM, which investigates new strategies to prevent transmission of leishmaniasis.
The second position is situated within the research group Pharmacometrics for Global Health and is focused on improving treatment and monitoring of drug-resistant tuberculosis. The project will be embedded in the international consortia UNITE4TB and EX-DR TB, both conducting phase 2 clinical trials of novel combination regimens including new chemical entities.
Both projects involve data-driven pharmacometric modelling and simulation techniques in combination with systems pharmacology, utilizing pharmacokinetic and pharmacodynamic data collected in clinical trials.
Deadline : 15 May 2026
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Pharmacokinetic-Pharmacodynamic – Oncology
A doctoral position is available within the Pharmacokinetic and Pharmacodynamic research unit at the Department of Pharmacy.
The overarching aim of this PhD project is to further develop data analysis methods that enable better treatment optimisation in oncology. Despite major therapeutic advances, many crucial decisions remain marked by considerable uncertainty: how to individualise dose and dosing schedules, how to balance efficacy against toxicity over time, and how to translate evidence from clinical trials to the heterogeneous patient populations seen in everyday clinical practice. At the same time, modern cancer treatment generates increasingly rich longitudinal data: clinical biomarkers, laboratory panels, adverse events, imaging‑based endpoints, and patient‑reported outcomes (PROMs). However, these data are often analysed using methods that do not fully exploit their dynamic nature or adequately address bias.
This project will develop and apply modelling tools that integrate pharmacometric methodology with causal inference to quantify relationships between treatment exposure, biomarkers, toxicity, and clinical outcomes. Key themes will include longitudinal modelling, simulation, and “virtual patient” tools for evaluating dosing strategies while accounting for potential bias arising from treatment adjustments or irregular follow‑up intervals. Particular emphasis will be placed on distinguishing true causal effects from validity issues commonly seen in datasets from patients with cancer, such as time‑varying confounding, immortal time bias, informative censoring, and selection bias. An additional objective is to strengthen validity beyond trial data, i.e., by characterising how patients in routine clinical practice differ from trial participants and how these differences are reflected in outcomes, adverse events, and PROMs.
The work will generate implementation‑relevant results, strategies for dose optimisation and monitoring, early detection of toxicity signals using longitudinal biomarkers, and benefit–risk summaries that are interpretable for clinicians and decision‑makers. Where relevant, model outputs will also be linked to health‑economic evaluations by quantifying the consequences of different dosing and monitoring strategies. Overall, the project aims to deliver both methodological improvements and practical impact by transforming longitudinal oncology data into robust evidence for precision dosing and optimisation of treatment strategies.
Deadline : 4 May 2026
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD student in evolutionary genomics and behavioral ecology
How do neural systems evolve to generate behavioral diversity? This project investigates the molecular and neural mechanisms underlying the diversification of mating behaviors using water striders as a model system. Several species in this group have independently evolved a mating strategy in which males defend territories and use water surface ripple signals to court females, who actively assess partner quality. This natural variation offers a powerful framework for uncovering how gene regulation in the brain drives behavioral innovation.
The PhD candidate will join the research group of Alberto Corral-Lopez at the Department of Ecology and Genetics. The position is fully funded for four years. More information about the research group is available at: https://www.uu.se/en/department/ecology-and-genetics/research/evolutionary-biology/corral-lopez-lab
A central aim of the PhD project is to develop and optimize a deep learning-based behavioral quantification pipeline that will serve as a core methodological resource for subsequent research in the program. Water striders are well-suited for this as their habitat is restricted to the water surface, which can be easily recreated in the laboratory with only two-dimensional movement that lends naturally to automated tracking. The student will evaluate available tools and establish a validated, reusable protocol for individual-level behavioral scoring across multiple experiments and species.
Building on this pipeline, the student will conduct behavioral experiments manipulating environmental contexts and combine the resulting data with brain transcriptomics to identify gene expression patterns associated with flexible mating decisions. This integrative approach will provide novel insights into how the nervous system regulates context-dependent behavior, and how gene regulation evolves to generate divergent mating phenotypes across species.
Deadline :28 May 2026
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Computational Materials Chemistry
We are looking for a motivated and ambitious student to conduct theoretical studies of ion transport in solid‑state battery materials. The goal of the project is to advance the fundamental understanding of the mechanisms governing ion transport in solid‑state polymer electrolytes for Li‑ion batteries.
The research will focus on developing new and computationally efficient strategies for multiscale modelling, enabling systematic studies of ion conductivity in complex battery materials on a large scale. Model‑generated data will be used to identify key relationships between material structure and ionic conductivity through advanced data‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future generation battery technologies.
The project is funded by the Swedish Research Council (VR), and will be carried out within the Structural Chemistry research program at Uppsala University which hosts both the Computational Materials Chemistry research environment and the Ångström Advanced Battery Centre (ÅABC). The PhD student will be an active member of both environments.
The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties can also include teaching and other departmental duties (no more than 20%).
Deadline : 30 April 2026
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD student in astrocyte-mediated spreading of Amyloid Protein-Lipid complexes
The Doctoral candidate key tasks will be to manage and carry out the assigned research project, participate in the LipAgg training and network activities, take PhD courses, write scientific articles and the PhD thesis, participate in national and international congresses and scientific meetings, undertake a research stay at an external research laboratory within the LipAgg network, and disseminate the obtained scientific results.
In particular, the Doctoral candidate enrolled in this position, will identify mechanisms by which astrocytes spread AP-L complexes and study if the AP-Ls induce astrocytic stress responses that affect their interplay with neurons. The candidate will expose cultures of human iPCS-derived astrocytes to fluorescently labelled AP-L complexes and analyze how the cells accumulate and spread the complexes over time using live cell imaging, immunocytochemistry, ELISA, Western blot, electron microscopy and other techniques. The Doctoral Candidate will be enrolled at Uppsala University under the supervision of Prof. Anna Erlandsson. The project includes a 7-month secondment at the University of Bordeaux and CNRS (France) to prepare the AP-L complexes under the supervision of Dr. Lucie Khemtemourian, as well as a 1-month secondment at BioArctic (Stockholm, Sweden).
Deadline : 4 May 2026
About Uppsala University, Sweden –Official Website
Uppsala University is a research university in Uppsala, Sweden. Founded in 1477, it is the oldest university in Sweden and all of the Nordic countries still in operation. It has ranked among the world’s 100 best universities in several high-profile international rankings during recent years. The university uses “Gratiae veritas naturae” as its motto and embraces natural sciences.
The university rose to pronounced significance during the rise of Sweden as a great power at the end of the 16th century and was then given a relative financial stability with the large donation of King Gustavus Adolphus in the early 17th century. Uppsala also has an important historical place in Swedish national culture, identity and for the Swedish establishment: in historiography, literature, politics, and music. Many aspects of Swedish academic culture in general, such as the white student cap, originated in Uppsala. It shares some peculiarities, such as the student nation system, with Lund University and the University of Helsinki.
Uppsala belongs to the Coimbra Group of European universities and to the Guild of European Research-Intensive Universities. The university has nine faculties distributed over three “disciplinary domains”. It has about 44,000 registered students and 2,300 doctoral students. It has a teaching staff of roughly 1,800 (part-time and full-time) out of a total of 6,900 employees. Twenty-eight per cent of the 716 professors at the university are women. Of its turnover of SEK 6.6 billion (approx. USD 775 million) in 2016, 29% was spent on education at Bachelor’s and Master’s level, while 70% was spent on research and research programs.
Architecturally, Uppsala University has traditionally had a strong presence in Fjärdingen, the neighbourhood around the cathedral on the western side of the River Fyris. Despite some more contemporary building developments further away from the centre, Uppsala’s historic centre continues to be dominated by the presence of the university.
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