Postdoctoral Fellowship (21) at Uppsala University, Sweden

The Department of Immunology, Genetics and Pathology at Uppsala University has a broad research profile with strong research groups focusing on cancer, genetic and autoimmune diseases. A fundamental idea at the department is to stimulate translational research and thereby promote closer interactions between medical research and health care. Research is presently organized in six research programs: Cancer Precision Medicine, Cancer Immunotherapy, Genomics and Neurobiology, Molecular Tools and Functional Genomics, Neuro-Oncology and Neurodegeneration and Vascular Biology. Departmental activities are also integrated with the units for Oncology, Clinical Genetics, Clinical Immunology, Clinical Pathology, and Hospital Physics at Uppsala University Hospital. The department has teaching assignments in several education programs, including Master Programs, at the Faculty of Medicine. The department has a yearly turnover of around SEK 550 million, out of which about two thirds derive from external funding. IGP has approximately 400 employees, out of which 100 are PhD-students, and there are in total more than 850 affiliated staff members. More information about the department’s activities can be found here: www.uu.se/en/staff/department/immunology-genetics-and-pathology

The project focuses on understanding how trace impurities present in recycled metal sulfate streams influence the synthesis and quality of precursor cathode active materials (pCAM) used in lithium-ion batteries. The research addresses challenges arising in circular battery value chains, where metals recovered through recycling processes may contain residual impurities that can affect precursor synthesis, particle morphology, and final material performance. The postdoctoral researcher will investigate ammonia-controlled co-precipitation of precursor materials and study how impurities such as Li⁺, Na⁺, F⁻, Mg²⁺, Cu²⁺ and Al³⁺ influence precipitation chemistry, metal partitioning, and precursor quality. The work will combine systematic experimental studies with advanced materials characterization to establish mechanistic understanding and define acceptable impurity ranges for precursor synthesis from recycled metal streams. The project will involve experimental design, synthesis and characterization of precursor materials, and analysis of impurity effects on precipitation behavior and particle properties. Selected precursor materials may also be evaluated through calcination and electrochemical testing to assess the potential impact of impurities on battery performance. 

The research project Genomics and Life Paths: Unraveling the genetic and social interactions shaping social, economic and health inequalities is funded by a four-year infrastructure grant from the Swedish Research Council. The purpose of the project is to increase our knowledge of the complex interplay between genes and environments in shaping our health and the position we occupy on the social and economic ladder. More specifically, we use the quickly increasing availability of large-scale genotype data across Europe, along with detailed register and survey data from several countries, to map out how specific genetic factors covary with and interact with our environmental exposures in producing variation in outcomes like health, education, income, political participation, social trust and other social behaviors. The project aims at continuing to develop a world-class interdisciplinary research environment at the forefront of social science genomics.

The project is now searching for three new postdoctoral fellows across three different research nodes – one at Uppsala university, one at Oslo university and one at Amsterdam UMC. This call is for the one position at Uppsala university (see here and here for the other two calls), though you may also apply for the other two. The three postdoctoral fellows will work closely in collaboration with each other as well as with other project members across all three nodes. A description of the project can be found här.

The successful candidates are expected to conduct research with a focus on one or more of the core questions of the project. To facilitate research collaboration, the candidates will also spend some amount of time at the other two nodes.

This is a three-year research project (2026-2029) funded by the Swedish Research Council that investigates how memories of mass violence are transmitted to younger generations in Cambodia. Through an ethnographic study of younger generations of the former Khmer Rouge, the project will explore how the youths inherit, interpret, and engage with memories of the Khmer Rouge while navigating past narratives in the context of authoritarianism in Cambodia.

We are looking for a researcher to lead the project and work independently to implement the day-to-day activities. Project tasks will include planning and conducting fieldwork, collecting and analyzing qualitative data, writing and disseminating research findings. The work should result in articles for publication in international peer-reviewed journals. Other project tasks may also be included.

We are offering a two-year postdoctoral position in microscopy and experimental cell biology, with a focus on image-based morphological profiling of patient-derived colorectal cancer 3D models.

The position is part of the ERKetype project, a European collaborative effort, which aims to develop digital twins as complex biomarkers for ERK signaling therapy. This role is central to that effort: you will generate the morphological profiling data underlying those complex biomarkers using Cell Painting, a state-of-the art multiplexed fluorescence imaging assay adapted to 3D cultures in the lab. Through the consortium, the findings will have direct potential for implementation in clinical trials.

You will work in a highly interdisciplinary environment spanning oncology, cell biology, imaging, bioinformatics and machine learning, with access to state-of-the-art robotic liquid handling, drug screening, and high-content microscopy infrastructure in the lab. We have an extensive repertoire of existing image-analysis pipelines and access to a large-scale data and analytics infrastructure, providing a strong foundation. This project provides a unique opportunity to deepen your competence bridging cell biology and data analytics, translating image-based data into meaningful biological insight.

The position offers excellent opportunities to build a strong and independent research profile, co-supervise students, contribute to grant applications, and develop your scientific leadership. You will be part of a dynamic research community in Uppsala with strong links to national and international scientific networks.

In this project, a digital modelling tool is being developed that links molecular simulations of lignin with process models for pulping. Using molecular dynamics (MD) simulations, the study analyses how the chemical structure of lignin affects accessibility, packing, and transport properties at the microscopic level. The MD results are integrated into a Physics-Informed Neural Network (PINN), where established physical relationships for diffusion, reaction, and mass balance are embedded in the model. The PINN model thus serves as an upscaling tool that translates molecular differences in the raw material into process-relevant parameters at the digester level.The project is carried out in collaboration with companies in the forest industry.

This project aims to develop unsupervised machine learning methods for extracting dynamical models of battery degradation from multimodal timeseries data, with a focus on interpretability. The underlying data comprises high-frequency acoustic emission (AE) measurements, paired with electrochemical measurements, from operating batteries. Such data are known to contain valuable information about complex electro-chemo-mechanical processes—such as particle fracture, interfacial delamination, and gas evolution—but are challenging to analyze. The project aims to move beyond black-box prediction by learning low-dimensional latent representations that capture these underlying physical processes. Examples of possible methodological components include self-supervised temporal representation learning for large volumes of unlabeled AE/electrochemical time-series data, switching state-space models that describe transitions between degradation modes, and neural ODE-based latent dynamics that connect multimodal signals sampled at different rates. Together, these tools will form an integrated framework that both extracts mechanistic insight from complex battery time series and enables interpretable battery health diagnostics and prognostics.

The exact details of the research project will be decided in a dialogue between the postdoctoral researcher and the supervisors Jens Sjölund (machine learning) and Leiting Zhang (battery sensing). The expected outcome is methodology and modelling tools for interpretable and generalizable analysis of large-scale timeseries data, applied to advance the fundamental understanding of battery aging and enable new diagnostic capabilities for real-time battery monitoring.

Cellulose, the most abundant biopolymer, is vital for sustainable materials and energy technologies. Its moisture-dependent electrical conductivity impacts bio-based electronics, sensors, insulation, and green composites. Reliable methods to measure and interpret this conductivity advance both fundamental science and applications such as energy storage, energy transmission, smart packaging, and biodegradable systems.

Understanding cellulose conductivity is especially critical for power transformer insulation used in HVDC (High Voltage DC) transmission system, where oil-impregnated cellulose plays a central role. The electrical conductivity of oil‑impregnated cellulose is strongly influenced by the specific transformer oil used and, more importantly, by the moisture content and the moisture exchange dynamics between oil and cellulose. Numerous studies show that even small amounts of moisture can dramatically increase the conductivity of impregnated pressboard. Despite this, a major knowledge gap remains: the behavior of the electric double layer (EDL) at the oil–cellulose interface. The EDL governs interfacial charge transfer, streaming electrification, and ultimately the macroscopic electrical response of the system. A deeper understanding of this interfacial charge structure is essential for predicting and controlling insulation performance under HVDC conditions.

This project aims to develop new experimental protocols together with a complementary theoretical framework to probe interfacial charge structure. A central objective is to uncover the origin, distribution, and impact of interfacial charges within the EDL—under static conditions as well as during oil flow, where streaming electrification becomes significant.

The Department of Linguistics and Philology carries out research and offers education in language studies and philology with a focus on the languages and cultures of Asia, the classical languages of Europe, general linguistics, and computational linguistics. Education is offered at the basic, advanced, and doctoral level. Some seventy persons work at the department. Together with students and partners, they create a multicultural and intellectually vibrant environment.

The research concerns development of a natural history pharmacometrics framework for Autosomal Dominant Polycystic Kidney Disease (ADPKD), an inherited genetic kidney disorder, to support development of new drug therapies for this inherited genetic kidney disorder. ADPKD is characterized by progressive renal cyst growth and kidney enlargement, decline in eGFR and requirement for renal replacement therapy. The framework development will be based on several longitudinal real world data bases including both traditional and newly derived covariates, biomarkers and endpoints of relevance for the disease. The research offers a possibility to mechanism-based model development utilizing non-linear mixed effects models for predicting disease trajectories and the population and individual level, supporting decisions on treatment options and trial designs. The project will be performed in international collaboration with multi-disciplinary experts (clin pharm, pharmacometrics, biostats and imaging) of our industrial partner. The candidate is expected to take a leading role in the collaborative meetings  and will be involved in writing scientific publications and part in the dissemination of research findings nationally and internationally. 

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoctoral fellow in Comparative Genomics

Deadline : 8 May 2026

View details & Apply

Rechargeable batteries are a key enabler for transitioning to a carbon-neutral future. Considering the enormous amounts of battery materials needed – e.g. for electrification of the vehicle industry – sustainability in materials and processes is of the highest importance. Regardless of the battery chemistry understudy, a circular battery value chain relies on efficient recycling. Current industrial recycling is mainly based on pyro- and hydrometallurgical methods, or a combination of those, to recover critical elements as well as elements that should not be released into the environment. Those methods are however energy, water, and chemical intense.

In this project, we aim to investigate and demonstrate an AI-driven fully automated and flexible robotized solution for the potentially more sustainable process of direct remanufacturing. First, a circular process is established in the form of a self-driving lab based on an industrial robot where Li-ion cells are manufactured, tested, analysed, disassembled, components washed, and remanufactured. The established process is optimized considering the chemical process solutions during electrode washing. Finally, the process is evaluated regarding potential for scaling to larger battery formats relevant for the vehicle industry. The research project is a collaboration between the Departments of Electrical Engineering and Chemistry within the battery initiative Compel.

Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would you like to work together with competent and inspiring colleagues in an international environment? Are you seeking an employer that offers safe and favourable working conditions? We welcome you to apply for a postdoctoral research position in Computerized Image Processing for data-driven precision medicine at the Department of Information Technology, Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are Uppsala University’s third largest department, with 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.