R&D Scientist (Machine learning for Bimolecular simulations)

AI Compute is a department in the AI & Algorithms expertise center that develops advanced AI compute solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid hardware architectures. The applications domains are diverse, ranging from AI for science, over AI for semiconductor process technologies, to automotive and health applications.

Within the AI Compute department, the AI Models group focuses on algorithmic and application research in the domain of AI and scientific simulations to drive SW-HW-Technology co-optimization and develop novel ML/Hybrid-AI methods for semiconductor processing and health applications. This position is within AI Models group based in Leuven, Belgium.

At imec, we are strengthening our AI Models team to build expertise in hybrid simulation approaches (Molecular dynamics and Machine learning) to advance in-silico drug discovery. If you are a senior AI/MD expert with experience in applying machine learning to scientific simulation and a passion for driving innovation, we want to hear from you. Join us in pushing the boundaries of AI, collaborating with industry leaders, and helping to develop the next generation of computational drug discovery tools.

What you will do

• You will be responsible for setting up molecular dynamics pipeline for biomolecular systems and developing novel machine learning models for research projects in the context of drug discovery.
• You will work on algorithmic analysis and performance optimization of such compute pipelines.
• You follow up on progress within research projects and are able to communicate the accomplished/ in-process work to internal and external stakeholders and adapt where needed.
• You will collaborate with other departments at imec to shape the compute roadmap for next-gen HPC system.

What we do for you

We offer you the opportunity to join one of the world’s premier research centers in nanotechnology at its headquarters in Leuven, Belgium. With your talent, passion and expertise, you’ll become part of a team that makes the impossible possible. Together, we shape the technology that will determine the society of tomorrow.

We are committed to being an inclusive employer and proud of our open, multicultural, and informal working environment with ample possibilities to take initiative and show responsibility. We commit to supporting and guiding you in this process; not only with words but also with tangible actions. Through imec.academy, 'our corporate university', we actively invest in your development to further your technical and personal growth.

We are aware that your valuable contribution makes imec a top player in its field. Your energy and commitment are therefore appreciated by means of a market appropriate salary with many fringe benefits.

Who you are

• You have a PhD, preferably in Physics, Computer Science, Engineering, Mathematics, or equivalent.
• You have proven experience with Molecular dynamics simulations, preferably for biomolecular systems.
• You have a strong foundational knowledge of machine learning and AI, backed by hands-on experience. Expertise in Diffusion and Flow models and/or Reinforcement Learning is a plus.
• You have experience with modern deep learning frameworks (PyTorch, Tensorflow, Jax).
• Good to have: experience with High Performance Computing and system performance optimization.
• You have affinity with academic research but also want to make a difference in today’s industrial and societal challenges.
• You are a team player and have strong communication skills.
• Your English is fluent, both speaking and writing.

Job details

Title: R&D Scientist (Machine learning for Bimolecular simulations)

Employer: imec

Location: Kapeldreef 75 Leuven, Belgium

Published: 2025-08-28

Application deadline: Unspecified

Job type: Researcher

Field: Computational Engineering, Computational Physics, Algorithms, Artificial Intelligence, Programming Languages, Software Engineering, Machine Learning, Systems Biology, Molecular Biology