This page lists the current PhD or PostDoc openings in our group. If you are interested in working 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.
In inverse materials design one wishes to discover novel materials in a targeting fashion. That is guided by systematic simulations of the expected properties of candidate structures, one aims to find the structure best matching a prescribed targeted property combination. A common approach is to employ a statistical surrogate (e.g. within a Bayesian Optimisation framework) such that the search only requires as few as possible of the expensive first-principle simulations. We will focus on approaches based on density-functional theory (DFT) simulation – an extremely common electronic-structure model.
Recent advances in the direction of multi-fidelity statistical models [1] as well as techniques to estimate numerical errors [2] in DFT simulations provide new opportunities for efficient, error-controlled Bayesian optimisation schemes for inverse materials design. In collaboration with similar ongoing projects in the group we will explore how these opportunities enable to accelerate the discovery of materials for electronics and mechanical devices.
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: Bayesian optimisation, Gaussian Process Regression, inverse design for PDEs in engineering or physics, 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 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 as well as multiple cross-disciplinary initiatives, such as the NCCR MARVEL. 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 autumn 2025 and hiring will be done on a continuous basis until a suitable candidate has been found. Note, that the chosen candidate will have to be accepted into one of the aforementioned doctoral schools before the contract can start.
In inverse materials design one wishes to discover novel materials in a targeting fashion. That is guided by systematic simulations of the expected properties of candidate structures, one aims to find the structure best matching a prescribed targeted property combination. A common approach is to employ a statistical surrogate (e.g. within a Bayesian Optimisation framework) such that the search only requires as few as possible of the expensive first-principle simulations. We will focus on approaches based on density-functional theory (DFT) simulation – an extremely common electronic-structure model. For DFT our recent work on algorithmic differentiation techniques within the density-functional toolkit (DFTK) is extremely promising to be exploited for such inverse design searches, as it enables to compute gradients of materials properties with respect to possible design parameters in a simple and efficient way.
In this project we will explore the opportunities of algorithmic differentiation to accelerate inverse materials design, e.g. by considering recent advances in first-order Bayesian optimisation procedures (e.g. [3]). Your work will be integrated with similar efforts in our group, e.g. to develop multi-task Gaussian Process surrogates[1] for materials modelling. Within ongoing collaborations with other materials simulation groups at EPFL your advances can be directly developed and tested within the scope of practical materials modelling problems.
You are motivated to tackle a challenging interdisciplinary research topic and push the state of Bayesian Optimisation methods for inverse materials design.
You obtained your PhD in statistics, computational mathematics, method development for computational materials discovery, method development for the modelling of electronic properties in solids or a related subject.
You have an excellent academic track record and demonstrated prior research experience in at least one of the following:
Quantum-chemical or materials simulations, computational materials design,
Bayesian optimisation, Bayesian experimental design or inverse problems with an application in physics or engineering simulations.
You enjoy collaborating with researchers from a diverse background and look forward to acquiring the broad skillset required for cross-disciplinary research in materials modelling.
You are experienced in working with larger scientific codes in a collaborative software development environment. You have a solid experience with the Julia programming language or you are fluent in a related language (Python, Matlab) and are curious to code in Julia.
You are fluent in written and oral English.
You enjoy occasionally supervising undergraduate students on topics related to your research.
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 as well as multiple cross-disciplinary initiatives, such as the NCCR MARVEL. 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 position will be a fixed-term position (CDD) for initially 2 years, renewable on a one-year basis. Further extensions depend on progress and the funding situation. For more information on working at EPFL see also the website on current employment conditions.
Screening of candidates is done on a rolling basis until a suitable candidate has been found.
The expected starting date is the second half of 2025, but can be negotiated.
| [1] | K. Fisher, M. F. Herbst, Y. Marzouk. Multitask methods for predicting molecular properties from heterogeneous data. Journal of Chemical Physics (2024). arXiv:2401.17898 |
| [2] | E. Cancès, G. Dusson, G. Kemlin and A. Levitt. Practical error bounds for properties in plane-wave electronic structure calculations SIAM Journal on Scientific Computing, 44, B1312 (2022). ArXiv:2111.01470. |
| [3] | J. Wu, M. Poloczek, A. Wilson, P. Frazier. Bayesian Optimization with Gradients. NeurIPS (2017). |
