University of Luxembourg
6 days ago
Postdoctoral Researcher in Computer Science University of Luxembourg in Luxembourg
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Country
Luxembourg
University
University of Luxembourg
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About this position
Postdoctoral Researcher in Computer Science, University of Luxembourg
The University of Luxembourg is recruiting a Postdoctoral Researcher in Computer Science to join the Faculty of Science, Technology and Medicine and the Department of Computer Science at the Belval Campus in Esch-sur-Alzette, Luxembourg. The position is part of the FNR AI-HPC 2025 BRIDGES project GenePPS, which focuses on using machine learning to predict gene perturbation effects for drug discovery and therapeutic target identification.
This postdoctoral role sits at the intersection of machine learning, genomics, and scientific computing. The successful candidate will lead work on hybrid foundation model and graph neural network methods for gene perturbation prediction, with a focus on efficient training strategies such as active learning and other data-efficient approaches. The project integrates large-scale single-cell foundation models with structured biological knowledge in genomic graphs, aiming to address limitations in perturbation modelling and to support flexible adoption under budget and time constraints.
The researcher will also carry out large-scale benchmarking and comparative evaluation across diverse single-cell datasets, collaborate closely with Helical-AI on scaling and optimization, and contribute to HPC-enabled industrial deployment in a production-grade pipeline. The work includes publishing in leading international conferences and journals in machine learning, computational biology, and AI for science, with translational impact supported by outsourced experimental validation.
Applicants should hold a PhD in computer science, machine learning, computational biology, or a closely related field. Strong research output, deep learning experience, knowledge of foundation models or graph neural networks, strong Python programming skills, and familiarity with GPU/HPC environments are important. Experience with biological data and interest in interdisciplinary AI-for-genomics research are advantageous. English fluency and strong teamwork are expected.
The appointment is a fixed-term full-time contract for 24 months. The advertised gross annual salary is EUR 87,306. Applications should include a CV, cover letter, PhD diploma or expected defense information, transcripts, list of publications, and 2-3 referees. Candidates must apply online through the HR system; email applications are not accepted. Early application is encouraged because applications are processed as they are received.
Funding details
Available
What's required
Applicants must hold a PhD degree in computer science, machine learning, computational biology, or a closely related field. The ideal candidate should have a strong research track record with publications in international venues in machine learning, AI for science, graph learning, or related areas; solid expertise in deep learning; experience with foundation models, transformer architectures, graph neural networks, representation learning, or large-scale training; a strong mathematical and algorithmic background; excellent Python programming skills and familiarity with modern ML tooling and reproducible research practices; experience training and deploying machine-learning models on GPU-based systems, with HPC experience advantageous; interest in interdisciplinary research at the interface of AI and genomics, with biological data or computational biology experience advantageous; strong teamwork skills; and fluent written and verbal English.
How to apply
Prepare a CV, cover letter, PhD diploma or expected defense confirmation, university transcripts, list of publications, and contact details for 2-3 referees. Apply online through the HR system; email applications will not be considered. Early application is encouraged because applications are reviewed upon receipt.
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