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University of Luxembourg

Postdoctoral Researcher in Machine Learning for Molecules and Materials University of Luxembourg in Luxembourg

Degree Level

Postdoc

Field of study

Computer Science

Funding

Fixed-term full-time postdoctoral contract for 12 months. The yearly gross salary is EUR 87,306 for every postdoctoral researcher at the University of Luxembourg.

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Country

Luxembourg

University

University of Luxembourg

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Keywords

Computer Science
Chemistry
Materials Science
Spectroscopy
Quantum Mechanics
Electronic Structure
Charge Transport
Physics
ML

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About this position

Postdoctoral Researcher in Machine Learning for Molecules and Materials at the University of Luxembourg, working in Professor Alexandre Tkatchenko’s group at the Faculty of Science, Technology and Medicine (FSTM), Belval Campus, Luxembourg.

The project focuses on developing machine-learning methods for molecules and materials, going beyond interatomic potentials and machine-learning force fields (MLFFs). The successful candidate will build models that predict electronic, optical, transport, and dynamical properties directly from atomic structure, with a long-term goal of creating transferable foundation models across chemical space. Research applications include molecular crystals, organic semiconductors, framework materials, biomolecular systems, spectroscopy, charge transport, and dynamics simulations, with collaborations involving academic and industrial partners.

Eligibility and profile: applicants should hold a PhD in physics, chemistry, materials science, computer science, or a closely related field. The role requires strong method-development experience, practical programming skills in Python, JAX or PyTorch, GPU training, and good scientific software engineering. A solid background in quantum mechanics and electronic-structure theory is expected, along with familiarity with MLFFs or related atomistic ML models. Independent research output through publications or open-source code is important, and excellent English is required.

Funding and contract: fixed-term full-time postdoctoral contract for 12 months. The yearly gross salary is EUR 87,306.

How to apply: submit a CV, cover letter, PhD diploma or expected defense information, transcript of university-level courses, publication list, and details of two referees. Applications must be submitted online through the HR system; email applications will not be considered. Early application is encouraged because applications are processed upon reception.

Location: Belval Campus, Luxembourg.

Funding details

Fixed-term full-time postdoctoral contract for 12 months. The yearly gross salary is EUR 87,306 for every postdoctoral researcher at the University of Luxembourg.

What's required

A PhD in physics, chemistry, materials science, computer science, or a closely related field. Applicants should have a track record of method development, having designed, implemented, and trained new machine-learning models or simulation methods rather than only applying existing ones. Strong practical skills in Python, a modern ML framework such as JAX or PyTorch, GPU training, and scientific software engineering practices are required. A solid understanding of quantum mechanics and electronic-structure theory (DFT and/or beyond), familiarity with machine-learning force fields or related atomistic ML models, evidence of independent research through publications or open-source code, and excellent English are expected. Desirable experience includes electronic-structure or spectroscopy ML, foundation-model pretraining, multi-task/transfer learning, charge-transport theory, electron-phonon or nonadiabatic dynamics, tight-binding or excited-state methods, molecular dynamics, molecular crystals, organic semiconductors, and large HPC systems.

How to apply

Apply online through the HR system using the provided application portal. Include a CV, cover letter, PhD diploma or expected defense information, transcript, publication list, and details of two referees. Email applications will not be considered.

More information can be found here

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