Determining Uncertainties Due to Dft Model

vor 7 Monaten

Lausanne, Schweiz EPFL Vollzeit

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 16,000 people, including over 12,000 students and 4,000 researchers from more than 120 different countries.

**Determining uncertainties due to DFT model parameters**:
**Mission**:

- One of the most widely used methods for modelling solid-state systems from first principles is plane-wave density-functional theory (DFT). A typical DFT simulation involves a number of modelling parameters, typically bundled in the form of modelling components such as the chosen pseudopotentials or the DFT functional. These parameters in turn can be determined employing some form of regression against higher-fidelity reference data, making them inherently uncertain. Propagating this uncertainty through a DFT simulation for observing its influence on simulation output quantities is to date hardly explored. A main obstacle for such research is in particular the challenging nature of DFT itself, involving at its heart a non-linear, non-convex optimisation problem. However, the recent implementation of algorithmic differentiation (AD) capabilities to the density-functional toolkit (DFTK), our in-house Julia-based DFT code, provides novel and so far unexplored opportunities for inference and uncertainty propagation.

**Principales tâches et responsabilités**:

- Carrying out research tasks related to the project mentioned above
- Presentation of research results at workshops and conferences
- Dissemination of results in form of software and journal articles
- Involvement in teaching and student supervision

**Profil recherché**:

- You are motivated to tackle a challenging interdisciplinary research topic and to substantially push the state of uncertainty quantification methods for density-functional theory.
- You have a strong background in Bayesian statistics and worked on uncertainty quantification for physics or engineering simulations.
- You have an excellent academic track record and demonstrated prior research experience working with Bayesian regression, multi-fidelity or multi-tasking methods or inverse problems.
- You enjoy collaborating with researchers from a diverse background and you look forward to acquiring the diverse skillset required for cross-disciplinary research in materials modelling.
- You enjoy pen and paper analysis, but you are not afraid to implement and test your ideas in practice.
- You have previously used Python, Matlab, R or Julia to conduct numerical experiments or test your ideas in non-trivial settings.
- You are fluent in written and oral English.
- You enjoy occasionally supervising undergraduate students on topics related to your research.

**Nous offrons**:
The activities of the MatMat group centre revolve around understanding modern materials simulations from a mathematical point of view - and to come up with ways to make such simulations faster, quantify their errors or make them more reliable. 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, explore your ideas and grow substantially your background in both the theory and practice of materials modelling. For this you have access to a stimulating community of researchers at EPFL's main campus beautifully located at the lake Geneva shore. For disseminating your work funds to attend suitable conferences and workshops as well as potential visits to our collaboration partners all over the world are provided.

**Date d'entrée en fonction**:
**Type de contrat**:
Durée déterminée (CDD)

**Durée**:
The position will be a fixed-term position (CDD) for initially 2 years, renewable on a one-year basis.

**Contact**:
**Référence**:
Poste No 2992

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