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European funding Represent the University of Luxembourg in international research forums, fostering collaboration and visibility Mentor PhD students and student assistants working on related topics
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More
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., 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
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of Health (LIH) is seeking a highly motivated Postdoctoral Researcher with specialized expertise in multi-omics data analysis. You will play a central role in analyzing large datasets from multiple large
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, reinforcement learning, robust or explainable models). • Knowledge of Network Digital Twin concepts. • Experience working with large, real-world datasets and building reproducible pipelines (data quality, missing
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questions related to infrastructure and development, and a thorough knowledge of applied econometrics The candidate should show some acquaintance in managing large datasets and non-traditional data, e.g. GIS
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the detection of zero-day vulnerabilities, and (2) enable continuous cybersecurity assessment of software. More specifically, the project will investigate the use of multimodal large language models and fuzzing
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engineering practices for machine learning Tabular machine learning Large language models on structured and semi-structured data Research Associate Role: Under the direction of their supervisor, the candidate
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verticals, Unmanned Aerial Vehicles, Integrated Satellite-Space-Terrestrial Networks, Quantum Communications and Quantum Information Networks, Spectrum Management and Coexistence, Tactile Internet, Earth