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Stefan Engblom
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PhD student in Computational Science with focus on Simulation-based Inference and Epidemiological Modeling Uppsala University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Dec 11, 2026
Country
Sweden
University
Uppsala University
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About this position
This PhD position is based at the Department of Information Technology, Uppsala University, where the research environment is described as international and strongly connected to both research and education. The department is large and interdisciplinary, with a substantial community of teachers and PhD students, providing a setting in which the doctoral candidate will work alongside experienced colleagues in computational science.
The project is centered on the development of computational and data-driven methods for epidemiological modelling and analysis. A key theme is the construction of stochastic models and Bayesian methods that combine epidemiological models with multiple data sources to improve reliable estimation and situational awareness. The work sits at the intersection of applied mathematics, statistics, machine learning, and computational science, with epidemiological applications as the common driver.
The advert highlights several possible research directions for the doctoral project. These include simulation-based inference, statistical validation and diagnostics, neural-network-based construction of informative prior distributions, and game-theoretic modelling of epidemics together with the value of information in public-health decision-making. The specific direction can be adapted to the student’s background and interests, making the project suitable for candidates with different quantitative profiles while remaining anchored in computational modelling and Bayesian inference.
The position is supervised by Stefan Engblom, and the contact details indicate close academic support during the project. The advertised environment emphasizes independent research work within a collaborative setting, with opportunities to contribute to advanced modelling methods and their use in epidemiological applications.
The doctoral student will primarily focus on graduate research training, with some possible departmental duties such as teaching and administration.
Funding details
Available
What's required
Applicants must meet the general entry requirements for doctoral studies by holding a Master’s (second-cycle) degree in Computational Science, Engineering Physics, Applied Mathematics, Statistics, Data Science, or a related subject, or by having completed at least 240 higher-education credits including at least 60 credits at Master’s level with an independent project worth at least 15 credits, or by having acquired substantially equivalent knowledge in another way. The successful candidate should have a solid background in mathematical modelling, statistics, computational methods, or a closely related area; experience in scientific programming and the ability to implement and evaluate computational methods are required. Good written and spoken English is required. Applicants should be able to work independently, take responsibility for their own research, collaborate effectively, and demonstrate strong analytical, problem-solving, and communication skills. Experience in Bayesian statistics, statistical inference or simulation-based inference, stochastic modelling, numerical methods, scientific computing, machine learning, neural networks, probabilistic machine learning, sequential Monte Carlo, particle filtering, state estimation, mathematical optimization, game theory, epidemiological modelling, Matlab, Python, R, C or C++, research software, simulation frameworks, or real-world data analysis is advantageous.
How to apply
Apply through the Varbi application portal using the link provided. In your application and personal letter, clearly explain how your background meets the requirements and aligns with the additional qualifications. Submit the application by 11 December 2026, citing reference UFV-PA 2026/3474.
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Professors
Professor at Uppsala University
Sweden
Research Interests
Statistics
Oral Pathology
Mathematics
Tumor Microenvironment
Tumor Progression
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Prepare your application
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