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machine learning, with established strengths in FinTech, the security of blockchain and decentralized finance, and the security of networking, vehicular networks, and cloud infrastructures. The group's work
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of both the basics and the latest developments of machine learning, in particular large language models and agentic framewoks will be considered a strong asset Language Skills: Fluent written and verbal
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have transformative potential across these research areas. We are now looking for an LLM Engineer in a shared position between VIB.AI's Machine Learning Expertise Unit and the Center for Neuroscience
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for robotic manipulation applications. You will collaborate with researchers working on flexible electronics, sensing systems, machine learning and robotics, and disseminate your results through scientific
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well as biologists. This in turn requires complete honesty and ease in revealing which fields the candidate is not an expert in, such that other team members can teach and support them Desirable: Having taken courses
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Fintech and AI with private and public stakeholders in Luxembourg and abroad Contribute to teaching activities at the Bachelor's, Master's, and PhD levels in topics such as AI and machine learning Provide
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tools for qualitative and quantitative analysis; experience and practice with machine learning and Artificial Intelligence are also considered assets Language requirements: The University of Luxembourg is
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to quality, integrity, creativity and cooperation. You have a profound knowledge of wireless communications, networking, and signal processing. You have at least intermediate knowledge of machine learning
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
machine-learning methods, you will analyze flame-turbulence interactions, pollutant formation, as well as unclosed terms relevant to LES modeling. The analysis involves the fluid dynamic as
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, engineered powders, including Cermetal and WC-Co-based materials, will be investigated as energy-absorbing media within the damping system. The development combines computational modeling, machine learning