This page lists the current openings in our group (PostDoc, PhD, summer projects). If you are looking for a PhD or PostDoc position with us, but no openings matching your profile, you are still welcome to submit a general inquiry.
In any case, please consider the application guidelines before you contact us.
Predicting the physical and chemical properties of materials today involves multi-stage computational pipelines that typically involve expensive data generation using first-principle models such as density-functional theory as well as the training of machine-learning surrogates. Each stage introduces both modelling and numerical approximations, which propagate and compound. As a result, trustworthy predictions require rigorous, quantitative error estimates across the entire workflow, which to date is largely lacking.
In recent work, our group has worked on quantifying simulation accuracy and improving models relying on algorithmic differentiation techniques[1]. In this work we rely on specific DFT models with calibrated uncertainty estimates, which were machine-learned from experimental data[2][3]. Taking this data-driven idea further neural operator techniques[4] offer both to improve accuracy of DFT predictions as well as the potential to similarly estimate model uncertainty from the training procedure itself. This offers numerous directions for follow-up research, which we want to explore with a motivated PhD student or PostDoc to expand our team. Possible topics include:
Machine-learned interatomic potential surrogates informed by error estimation techniques in first-principle density-functional theory (DFT) simulations.
Error estimation of DFT model predictions
Constructing novel DFT models using data-driven techniques (machine-learned XC functionals)
Algorithmic differentiation techniques to enable sensitivity analysis and goal-oriented uncertainty quantification
You are highly motivated and want to become an independent researcher in a fascinating interdisciplinary field, working towards faster and more reliable methods for discovering the materials of tomorrow.
You have a strong sense of autonomy and independence, but also enjoy being part of a diverse team.
You have completed a Master (or 4-year Bachelor) in physics, mathematics or a related subject. Candidates who will complete their degree within the next months are also welcome to apply.
Your academic record is strong and underpins your potential to become an excellent researcher.
The ideal candidate has a prior background in multiple of the following subject areas: Gaussian Process Regression, Bayesian techniques in uncertainty quantification, operator learning, mathematical structure of quantum physics, condensed matter physics, atomistic modelling, atomistic machine learning, electronic structure theory.
While you may not have expertise in all of these domains you look forward to acquiring expertise in electronic structure theory and atomistic modelling.
You have a strong interest in numerical methods, their implementation and application to physics and materials simulations.
You enjoy programming and implementing algorithms and have solid experience in a programming language such as JAX, pytorch or Julia.
You are fluent in written and oral English.
Bonus skills for this application are considerable experience in sustainable software engineering or high-performance computing.
The activities of the MatMat group revolve around understanding modern materials simulations from a mathematical point of view –- and to come up with ways to make such simulations faster and quantify their errors. You will become part of a young and energetic team, fully integrated with both the mathematics and the materials institutes. Within the proposed topic you will be able to bring in your prior expertise, but also be able to get to know the exciting theory and practice of material modelling. EPFL's main campus is beautifully located at the lake Geneva shore hosting a stimulating community of interdisciplinary-minded researchers. Funds to disseminate your work at suitable conferences as well as potential visits to our international network of collaboration partners are provided.
The current regulations regarding salary and working conditions of PhD students at EPFL can be found on the detailed websites on salary, employment conditions and PhD admission criteria.
The position is available from january 2027 and hiring will be done on a continuous basis until a suitable candidate has been found. Note, that for PhD candidates, the chosen candidate will have to be accepted into one of the aforementioned doctoral schools before the contract can start.
| [1] | N. F. Schmitz, B. Ploumhans and M. F. Herbst. Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters. npj Computational Materials 12, 6 (2025). DOI 10.1038/S41524-025-01880-3 (Preprint: https://arxiv.org/abs/2509.07785) |
| [2] | Mortensen, J. J., K. Kaasbjerg, S. L. Frederiksen, J. K. Nørskov, J. P. Sethna, and K. W. Jacobsen, Phys. Rev. Lett. 95 (2005), DOI 10.1103/phys-revlett.95.216401 |
| [3] | Hansen, T., J. J. Mortensen, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 112, 7, 075412 (2025). |
| [4] | N. Bosch, N. F. Schmitz, M. F. Herbst. Euclidean Fourier Neural Operators. (Preprint: https://arxiv.org/abs/2608.28425) |
We are always looking for outstanding students to join our group over summer as part of the EPFL excellence in engineering (E3) programme. The program is open to Bachelor and Master students in science, mathematics or engineering degrees. To submit your application and for more information please see the E3 website. Deadlines are usually in autumn for an internship in the following summer.
