24 high-performance-quantum-computing-"https:"-"https:"-"https:"-"https:"-"Fraunhofer-Gesellschaft" Postdoctoral positions in Belgium
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computational genomics of Alzheimer’s disease. The Sleegers lab is an international team committed to increasing insights into the complex genetics of Alzheimer’s disease and to investigating the translational
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Computer Science, Information Theory, Physics or related fields High level of mathematical maturity Experience with topics related to quantum LDPC codes and decoding algorithms, or demonstrated ability and
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(French, German, English, Dutch) for Belgians Scholarships for EU citizens: Fulbright Schumann Programme for (pre)doctoral or post-doctoral research related to EU affairs or the US-EU Transatlantic Agenda
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description → Apply before 23/12/2025 (DD/MM/YYYY) 23:59 (Brussels Time) → Faculty
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Researcher (R2) Country Belgium Application Deadline 2 Mar 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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field of molecular and computational genomics of Alzheimer’s disease. The Sleegers lab is an international team committed to increasing insights into the complex genetics of Alzheimer’s disease and to
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). Familiarity with high-performance computing (HPC) and software containers. Knowledge of cancer genomics or regulatory biology is a plus. Mindset: Ability to work independently while thriving in a
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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rich models without increased cost or workflow complexity. By leveraging AI models trained on high-end imaging modalities, this project will bridge the gap between routine practice and high-fidelity