157 assistant-professor-computer-science-data-"https:"-"https:"-"https:" positions in Luxembourg
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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for visualisation of environmental data Your profile A master's degree in data science, geography, environmental sciences, urban planning, computer science, design, or a related field Strong interest in
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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Humanities, Education and Social Sciences (FHSE
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) at the University of Luxembourg contributes multidisciplinaryexpertisein the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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the University’s mission in academic continuing education, staff development and training for Luxembourg, with strong expertise in digital learning. Reporting to the future Head of ULCC, the Study Programme
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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The successful candidate will join the young, vibrant, and interdisciplinary FINATRAX Research Group, which builds bridges between business research and information systems engineering. The group
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within the project AI4TECS Writing a doctoral dissertation in computer science Publishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
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of Computer Science, Faculty of Science, Technology and Medicine. The ECI PhD candidate will perform prioritized Non-Targeted Analysis across diverse water matrices and case studies, while the AI4Science PhD will
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embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in this project share conceptual similarities with adaptive network and