Doctoral Researcher in Uncertainty-Aware Machine Learning Force Fields (University of Luxembourg)

Doctoral researcher in Uncertainty-Aware Machine Learning Force Fields Contract Type: Fixed Term Contract Duration: 36 Month About us The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in. Your role The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs), uncertainty quantification, and atomistic simulations within the FNR-funded UMLFF project. MLFFs have transformed atomistic simulations, offering quantum-chemical accuracy for large systems. However, they currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated active learning and create broad, reliable MLFFs. You will join the Theoretical Chemical Physics group led by Prof. Alexandre Tkatchenko, supervised by Dr. Igor Poltavskyi, working on: As a Ph.D. candidate, you will work on a cutting-edge, multidisciplinary research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware loss functions Study calibration and post-calibration for predictive uncertainty Integrate uncertainty modules into MLFF architectures Detecting Extrapolation and Low-Reference Regimes Analyze MLFF feature spaces to find out-of-distribution atomic environments Use chemical neighborhood representations to detect sparse or unseen cases Combine ML features with chemical descriptors to enhance uncertainty robustness Building Uncertainty-Aware General Purpose Models Merge developments into a unified, efficient MLFF architecture Train MLFFs on large, diverse datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs into active learning frameworks Explore automated dataset generation for molecules and materials Contribute to open-source tools for automated MLFF training Your profile Essential qualifications: Master’s degree in Physics, Chemistry, Materials Science, Computer Science, or related fields Strong background in theoretical/computational physics or chemistry Experience with machine learning, Python, and modern ML frameworks like PyTorch/JAX Genuine interest in MLFFs, simulation methods, and foundational ML research Desired skills: Experience with atomistic simulation codes: ASE, FHI-aims, VASP, CP2K Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification Familiarity with HPC environments Language Requirements: Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission – Doctoral Candidates. We offer Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR) A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs … How to apply Please prepare a single PDF file including: Curriculum Vitae, including relevant coursework, ML experience, and publications, if any Cover letter presenting your motivation for this doctoral thesis topic, and explaining how your qualifications and aspirations align with its academic focus Transcript of all modules and results from university-level courses taken Contact information for two references Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students. General information: Contract Type: Fixed Term Contract 36 Month Work Hours: Full Time 40.0 Hours per Week Location: Limpertsberg Campus Internal Title: Doctoral Researcher Job Reference: UOL07998 The yearly gross salary for every PhD at the UL is EUR 41976 (full time). If you apply for this position please say you saw it on Physicaloxy

Doctoral researcher in Uncertainty-Aware Machine Learning Force Fields

Contract Type:

Fixed Term Contract

Duration:

36 Month

About us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine.
Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in.

Your role

The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs), uncertainty quantification, and atomistic simulations within the FNR-funded UMLFF project.

MLFFs have transformed atomistic simulations, offering quantum-chemical accuracy for large systems. However, they currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated active learning and create broad, reliable MLFFs.

You will join the Theoretical Chemical Physics group led by Prof. Alexandre Tkatchenko, supervised by Dr. Igor Poltavskyi, working on:

As a Ph.D. candidate, you will work on a cutting-edge, multidisciplinary research program that brings together physics, chemistry, and machine learning. Your research tasks will include:

Uncertainty Estimation in Deep Neural Networks for MLFFs

Implement and test uncertainty-aware loss functions
Study calibration and post-calibration for predictive uncertainty
Integrate uncertainty modules into MLFF architectures

Detecting Extrapolation and Low-Reference Regimes

Analyze MLFF feature spaces to find out-of-distribution atomic environments
Use chemical neighborhood representations to detect sparse or unseen cases
Combine ML features with chemical descriptors to enhance uncertainty robustness

Building Uncertainty-Aware General Purpose Models

Merge developments into a unified, efficient MLFF architecture
Train MLFFs on large, diverse datasets across broad chemical space
Evaluate models through molecular dynamics, simulations, and benchmarks

Active Learning in Configurational and Chemical Spaces

Integrate uncertainty-aware MLFFs into active learning frameworks
Explore automated dataset generation for molecules and materials
Contribute to open-source tools for automated MLFF training

Your profile

Essential qualifications:

Master’s degree in Physics, Chemistry, Materials Science, Computer Science, or related fields
Strong background in theoretical/computational physics or chemistry
Experience with machine learning, Python, and modern ML frameworks like PyTorch/JAX
Genuine interest in MLFFs, simulation methods, and foundational ML research

Desired skills:

Experience with atomistic simulation codes: ASE, FHI-aims, VASP, CP2K
Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification
Familiarity with HPC environments

Language Requirements:

Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission – Doctoral Candidates.

We offer

Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR)
A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

How to apply

Please prepare a single PDF file including:

Curriculum Vitae, including relevant coursework, ML experience, and publications, if any
Cover letter presenting your motivation for this doctoral thesis topic, and explaining how your qualifications and aspirations align with its academic focus
Transcript of all modules and results from university-level courses taken
Contact information for two references

Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered.

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information: