Stefan Engblom

Stefan Engblom

Professor in Scientific Computing

Stefan Engblom’s personal web-page

I am…
…the Program chair of Applied Scientific Computing…
…the Director of eSSENCE…
…a SciLifeLab Group Leader…
…coordinating the TDB Seminar Series…
…the current faculty advisor of UPPSALA SIAM Chapter.

Find me at the Mathematics Genealogy Project.

Research

I am particularly interested in Scientific Computing in the intersection with Data-driven research and Data Science. I have extensive experiences in many aspects of Scientific Computing in general, in Numerical Modeling and -Analys in particular, as well as in High-Performance Computing. My main focus of applications are in the Biosciences at broad, but I’ve also taken an interest in traditional computational Engineering applications.

Current active research projects include Bayesian approaches for compute intensive data-driven models in epidemics, including in particular prediction, and multiscale modeling and parameterization of living cells, where spatial stochasticity is an important aspect of the modeling.

In case you are interested in doing a project work or a MSc-thesis, please feel invited to contact me for further discussions

Open project proposals:

PhD position in Computational Science with focus on Simulation-based inference and Epidemiological Modeling

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.

More details about the project can be found here.

Please apply using the online application system linked above. Application deadline 11th of December 2026.

Postdoc position in computational modeling and simulation of spreading processes in population-scale multilayer social networks

Fully funded Postdoc position in a novel collaboration between the Data Science and Intelligent Decision Making and the Applied scientific computing and High-performance computing eSSENCE research programmes. The postdoc will join the Information Laboratory (InfoLab), an interdisciplinary research group where we do basic and applied research on the extraction of knowledge from social data.

Please apply using the online application system linked above. Application deadline 16th of November 2026.

Computational Epidemics driven by Data
In this line of research we are interested in the trade-offs between data and detailed so-called mechanistic models. The application focus is in epidemics and concerns modeling under uncertainties and supporting risk-based decisions. Part of the work will be done in URDME.

More details can be found here.

Computational cell population models: multiscale and multiphysics modeling
There are three suggested openings for projects within the software framework URDME. Suggestions include advanced new modeling, improving simulation efficiency and flexibility, as well as mathematical analysis.

More details can be found here.

Latest publications 2026

See also: my Google Scholar profile, or DiVA records for Author: Stefan Engblom. Alternatively, list of publications as a pdf.

  • D. V. Shridar and S. Engblom: The impact of strain heterogeneity on genomic profiling accuracy in mixed-strain Escherichia coli metagenomes
    • bioRxiv preprint doi.
  • B. Berglund, D. V. Shridar, M. Wrande, J. Jerlström-Hultqvist, S. Engblom, L. Sandegren, and S. Koskiniemi: Wastewater is not a reliable proxy for clinical carbapenem resistance prevalence in E. coli
    • medRxiv preprint doi.
  • S. Widgren, I. Ewerlöf, A. Comin, R. Johansson, S. Engblom: Bayesian modelling of herd-level infection dynamics in cattle: Local spread as the primary driver of Salmonella Dublin persistence on Öland
  • A. T. Nordman, S. Engblom, D. van der Spoel: Bayesian modeling of polarizable water: Lessons for force field development
    • In J. Chem. Theory Comput., 22(11):5435–5447 (2026). (doi)
  • E. Blom, Q. Peng, L. Pomfret, R. Mort, S. Engblom: Towards Data-Driven Modeling of Cell Cycle and Wound Closure Processes
  • E. Blom, S. Engblom: DLCM: a versatile multi-level solver for heterogeneous multicellular systems
    • Available via arXiv.
    • Reproduce the numerical experiments using URDME.

Latest news and activities 2026