Postdoctoral Researcher in Computer Science

Contract Type:

Duration:

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.

The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and foundation-model experiments.

Your role

We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable prediction of gene perturbation effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate therapeutic target identification.

GenePPS aims to overcome current limitations of perturbation modelling by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic foundation models and HPC-enabled model deployment, ensuring that methodological advances are developed with direct translational and scalability considerations.

Responsabilities:

• Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approaches
• Conduct large-scale benchmarking and comparative evaluation of gene perturbation models across diverse single-cell datasets
• Collaborate closely with Helical-AI on scaling, optimization, and integration of the developed models within an HPC-enabled industrial pipeline
• Publish research results in leading international conferences and journals in machine learning, computational biology, and AI for science

The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and translational impact. Close collaboration with Helical-AI will ensure that developed models are integrated into a production-grade platform. The results will be evaluated in a real-world target identification workflow, including outsourced experimental validation.

Your profile

We are looking for a candidate who is passionate about advancing scientific knowledge at the intersection of machine learning and genomic perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject.

More precisely:

• PhD degree in computer science, machine learning, computational biology, or a closely related field
• Strong research track record demonstrated by publications in international venues in machine learning, AI for science, graph learning or related areas
• Solid expertise in deep learning, with experience in at least one of the following: foundation models, transformer architectures, graph neural networks, representation learning, or large-scale training
• Strong mathematical and algorithmic background, with the ability to design and analyse novel machine-learning methodologies
• Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices
• Experience training and deploying machine-learning models on GPU-based systems; familiarity with HPC environments is an advantage
• Interest in interdisciplinary research at the interface of AI and genomics; prior experience with biological data or computational biology is an advantage
• Strong teamwork skills and willingness to collaborate closely with academic and industrial partners.
• Fluent written and verbal communication skills in English

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

Applications should include:

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 24 Month
• Work Hours: Full Time 40.0 Hours per Week
• Location: Belval Campus
• Internal Title: Postdoctoral researcher
• Job Reference: UOL08452

The yearly gross salary for every Postdoctoral Researcher at the UL is EUR 87306 (full time).