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international calls for workshop, related review processes and the resulting working group programs Actively ties promote synthesis science activities in the University and Luxembourgs research institutes
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sciences, medicine), with a primary focus on the four year Midwifery Sciences Bachelor program. The holder will be involved in the selection of students, the establishment of the training plan, preparation
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excellence. Candidates should propose an innovative research program that fits within the global themes of the IGMM and addresses a fundamental biological question. Such a project can be positioned
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, completed by large test campaigns on cell prototyped samples, to prove the advantages of Li-ion battery cells with Reference Electrode in terms of robustness, durability and safety. You will plan and organise
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controlled via structural phase transitions or external fields. The successful candidate will develop and apply a range of theoretical and computational methods based on first-principles electronic structure
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-related inquiries within LCSES Coordinating, supervising, and enhancing the capabilities of the Data Science team Represent the Data Science Team in LCSES management board For further inquiries, please
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of the biological bases of normal and pathological behaviors. Required Qualifications: PhD or equivalent experience in neuroscience, computer science, engineering or a related field. Proven experience in
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management is a plus. Rigor, motivation, scientific creativity and originality, writing skills, conciseness, sense of priority and ability to work in a team. Languages: fluent command of English, knowledge
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:•Basic knowledge of cellular and molecular biology.•Good level of English language, which will be the working language for the project.•Critical thinking and decision making.•Adaptability.•Ability to work
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interdisciplinary, and together we contribute to science and society. Your role We seek a highly motivated bioinformatician or computational biologist who is well versed in the statistical and machine learning