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generation and semantic search across historical statistics publications. Successful PhD candidates will extensively explore and analyse the suitability and potential benefits of LLMs (and other machine
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of the following fields: Electrical and Electronics Engineering, Telecommunications Engineering, or Computer Science/Engineering. Experience: The candidate should have strong digital signal processing and
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will
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interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering
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· PhD in Aerospace Engineering, Mechanical Engineering, Applied Physics, or a closely related discipline. · Strong academic background in computational mechanics, fluid dynamics, electromagnetics
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institutions within Luxembourg. The PhD student will be enrolled in the Doctoral School in Science and Engineering at the University of Luxembourg, and follow a vibrant PhD program that includes training in
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at the heart of Europe, the group consists of doctoral and post-doctoral researchers from diverse backgrounds (e.g., economics, business, technology), united in pursuit of sustainable solutions that positively
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of ultra-fast MLIPs to simulate the nanoindentation of cemented carbides, a material of industrial relevance, in agreement with and beyond experimental nanoindentation results. Education PhD in materials
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: Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg · PhD enrolment: University of Luxembourg, Belval, Luxembourg · Candidates must be available to start their position in
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Qualification: The candidate should possess a MSc. Degree or equivalent in Engineering, Computer Science, or related fields. Experience: The ideal candidate should have some knowledge and experience