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systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents for autonomous vehicles or robots operating in dynamic
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and www.spacer.lu The candidate should develop the following tasks: Conduct cutting-edge research in learning-based and/or model-based control and/or perception strategies for dexterous robotic
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procedures Innovative teaching experience and interest to develop these further Fluency in English is required; good command of French, Luxembourgish and / or German is considered an asset We offer
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Biology, Data Science, or a related field. Technical Proficiency: Strong command of R. Knowledge of Python is an asset. Multi-Omics Expertise: Proven experience in the analysis and integration of diverse
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targets. Key Responsibilities: Perform deep immunophenotyping of CD8 T cell subsets in cohorts of RBD participants and healthy controls Screen for autoantibodies using genome-scale protein microarrays and
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control) using real operator-grade datasets (traffic indicators, network KPI's, configuration logs and energy measurements when available). • Investigating and combining multiple energy-saving levers (e.g
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control, documentation, automation) Soft skills Ability to work effectively in interdisciplinary teams spanning computational and experimental research Strong analytical and problem-solving skills, with
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multilingual university. Therefore, the command of at least two of the University languages is necessary: French, English, German We offer Multilingual and international character. Modern institution with a
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subsets in cohorts of RBD participants and healthy controls Screen for autoantibodies using genome-scale protein microarrays and focus arrays Differentiate iPSCs into midbrain dopaminergic neurons and
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evaluating adaptive Medium Access Control (MAC) and network‑layer protocols to enhance performance in shared spectrum environments. Developing AI‑driven methods for real‑time interference prediction and