PhD student in Computational Science with focus on Simulation-based inference and Epidemiological Modeling Full-time NEW

PhD Student in Computational Science with Focus on Simulation-based Inference and Epidemiological Modeling

Are you interested in working with research in Computational Science, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a PhD position 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 currently 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.

Project description: The project focuses on the development of computational and data-driven methods for epidemiological modelling and analysis. A central component is the development of stochastic models and Bayesian methods for combining epidemiological models with different types of data, with the aim of enabling reliable estimates and situational awareness. The project offers several possible research directions, including statistical validation and diagnostics for simulation-based inference, the use of neural networks to construct informative prior distributions, and game-theoretic modelling of epidemics and the value of information in decisions concerning public health interventions. The common foundation is computational modelling and Bayesian inference, while the specific research direction can be adapted to the doctoral candidate's background and interests. The project thus offers opportunities to work at the intersection of applied mathematics, statistics, machine learning and computational science, with epidemiological applications providing a common focus.

Duties: The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.

Requirements: To meet the general entry requirements for doctoral studies, you must:

• hold a Master's (second-cycle) degree in Computational Science, Engineering Physics, Applied Mathematics, Statistics, Data Science, or a related subject, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master's level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject's general study plan.

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 also required, as the research and scientific communication will be conducted in English.

The position requires the ability to work independently and take responsibility for one's own research, as well as to collaborate effectively with others. The candidate should have strong analytical and problem-solving skills, a structured approach to work, and the ability to communicate research results clearly.

Additional qualifications: Experience in one or more of the following areas is considered an advantage:

• Bayesian statistics, statistical inference or simulation-based inference.
• Stochastic modelling, numerical methods or scientific computing.
• Machine learning, particularly neural networks and probabilistic machine learning.
• Sequential Monte Carlo methods, particle filtering or state estimation.
• Mathematical optimization or game theory.
• Epidemiological modelling or other applications involving dynamical systems.

Experience with programming in Matlab, Python, R, C or C++ is beneficial. Previous experience with research software, simulation frameworks or the analysis of real-world data is also an advantage.

In your application and personal letter, please state clearly how your background fulfills the requirements and aligns with the additional qualifications.

About the employment: The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date 1 February 2027 or as agreed. Placement: Uppsala.