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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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particular focus on digital methods and tools. The C²DH's ambition is to venture off the beaten track and find new ways of doing, teaching and presenting contemporary history of Luxembourg and the history
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datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference
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and very good knowledge in quantitative and qualitative research methods Good knowledge of statistical software (e.g. SPSS or STATA or R or JASP) Strong commitment and the ability to work in a team
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(FSTM) 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
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. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various
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be expected to teach advanced courses in Artificial Intelligence—particularly in machine learning, statistical learning, natural language processing (NLP), symbolic AI, computer vision, and related
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. Collaborate with people with diabetes, diabetologists, software engineers, and clinical psychologists to develop and validate prototype tools. Contribute to qualitative and mixed-methods research to ensure user
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chemistry The ideal candidate will have a strong background in analytical chemistry, derivatization methods, extraction as well as gas chromatographic techniques. Further experience in the handling of extra
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! Education Master's degree (Bac+5) in telecommunications, computer science, or a related field, with an interest in AI, cognitive networks, or connected vehicles. Experience and skills Prior experience with