15 design-"https:"-"https:"-"https:"-"U.S" Postdoctoral research jobs at University of Luxembourg
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focus on Computer Vision, pattern recognition, and visual generative modeling. CVI2 offers the opportunity to contribute to a diverse portfolio of projects, including collaborations with industrial
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of publications Contact information of at least two referees Sketch of planned research, publication activities and ideas for a course design of an introductory course to inclusive education at MA – level (maximum
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pilots. The role combines experimental device R&D, modelling-supported design decisions, and rigorous measurement work. You will work closely with both academic and industrial supervisors in a small
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of young people. In Luxembourg’s highly diverse society, there is an urgent need to understand how social background shapes life chances: your work will speak directly to that. You will also help design and
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translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and
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(SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Europe by
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activities, including the design and implementation of novel methods, experimental evaluation, and dissemination of results through high-quality scientific publications. Beyond technical contributions, you are
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in counter drone swarm formation and defense Design and conduct experiments using both simulation environments and real-world deployments with the nano-drones of the University’s SwarmLab Disseminate
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innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds
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precisely, the postdoctoral researcher will: Develop and implement uncertainty-aware MLFF architectures Design and test loss functions, calibration methods, and training strategies Analyze feature-space