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applied research to design solutions that address real-world challenges and create positive impact. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute
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: https://www.list.lu/ How will you contribute? Contributing to multiple R&D projects in the AIRA team, an R&T Scientist position is opened to contribute with algorithms and models in data-driven and
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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wireless communications systems. For details, you may refer to the following: https://wwwen.uni.lu/snt/research/sigcom We’re looking for people driven by excellence, excited about innovation, and looking
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proximity operations in collaboration with Redwire Space Luxembourg. The candidate will carry a leading role in this area and support PhD candidates in their thesis research. The candidate will work closely
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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are looking for a recognised business development
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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of particle-handling systems for the space environment, including the development of robust design criteria · Couple physics-based models and numerical simulations with optimization algorithms
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and participate in the dissemination of the results through visualizations, publications and presentations. Key Skills, Experience and Qualifications Education: PhD in Bioinformatics, Computational
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under