Postdoctoral Fellow – Atomistic Simulations and AI Materials Design

Organisation/Company Vrije Universiteit Brussel Department Materials and Chemistry (MACH) Research Field Physics Engineering – Materials engineering Engineering – Chemical engineering Chemistry Computer science Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Final date to receive applications 28 Feb 2026 - 23:00 (Europe/Brussels) Country Belgium Type of Contract Temporary Job Status Full-time Hours Per Week 38 Offer Starting Date 1 Apr 2026 Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

The SUME research group at Vrije Universiteit Brussel (VUB), invites applications for a Postdoctoral Fellow position in the fields of atomistic simulations, machine‑learned force fields, and artificial intelligence (AI). The successful candidate will lead the development of a computational platform that unifies first‑principles methods, classical molecular simulations, and cutting‑edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials.

The research spans quantum mechanics, statistical physics, and deep learning and aims to enable AI‑guided predictions of synthesizable and functional materials such as energy storages, catalysts, smart‑alloys, energy‑relevant compounds. The position is embedded in an interdisciplinary and collaborative environment with active interactions across experimental groups and national and European laboratories.

E-mail

Research Field Engineering Education Level PhD or equivalent

Research Field Physics Education Level PhD or equivalent

Research Field Chemistry Education Level PhD or equivalent

• A PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computer Science, or a related field.
• Demonstrated experience in one or more of the following:
  Density Functional Theory (DFT), machine‑learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs).
• First‑principles atomistic simulations with packages such as VASP, Quantum ESPRESSO, LAMMPS, GROMACS.
• Machine‑learned interatomic potentials.
• Structure‑property prediction using GNNs.
• LLM fine‑tuning and prompt engineering (e.g., Hugging Face, OpenAI, AtomGPT).
• Workflow tools (e.g., ASE) and HPC environments.
• Software development in Python, Git‑based version control, and Conda packaging.
• Data integration and surrogate modeling using experimental and computational datasets.
• Interdisciplinary collaboration and mentoring of MSc students and PhD researchers.

• Conduct high‑throughput DFT calculations and manage large‑scale materials datasets.
• Develop GNN architectures for predicting materials properties from atomic graphs.
• Train and deploy machine‑learned force fields for MD simulations and rapid screening.
• Fine‑tune or pre‑train LLMs for generation and analysis of materials structures, synthesis protocols, and characterization outputs.
• Build pipelines for combining experimental and simulated data for inverse design.
• Provide real‑time computational feedback to experimental collaborators for synthesis and characterization.
• Lead manuscript writing, conference presentations, and contributions to open‑source repositories.
• Mentor Masters and PhD students, and participate in grant proposal development.

• Collaborate on interdisciplinary proposals.
• Engage with experimental groups, and industry partners.
• Attend international conferences and contribute to global research communities.
• Access to cutting‑edge computing clusters and experimental characterization tools.

ENGLISH Level Excellent

A postdoctoral position (100%) for a period of 3 years. Starting with a 1-year trial period. Challenging, dynamic and stimulating work in three internationally renowned research groups. State‑of‑the‑art facilities and equipment. A multicultural and international work environment. An international network dealing with state‑of‑the‑art research. Working and living in Brussels, the Capital of Europe, one of the most cosmopolitan cities of the world.

A vibrant and charming city, which combines history, modernity, arts and gastronomy.

Interested candidates should submit a single PDF file containing their curriculum vitae and three recent publications to Prof. Dr. Ir. Mesfin Haile Mamme via email , with the subject line “HORTA AIMD”. Vrije Universiteit Brussel (VUB), Dept. of Chemistry and Materials (MACH), Research group of Sustinable Materials Engineering (SUME), Faculty of Engineering Sciences, Pleinlaan 2 - 1050 Brussel -  ;