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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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Langelandsgade 140, 8000 Aarhus C, and the area of employment is the Department of Chemistry, Aarhus University and related departments. Further information about the position may be obtained from Associate
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collaboration with staff scientists in the team - publish scientific articles based on the data you generate - present research findings at research meetings and conferences Your profile: We seek candidates
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more about the department at www.es.aau.dk. Your work tasks The PhD project is part of a bigger Novo Nordisk Foundation (NNF) New Exploratory Research and Discovery grant entitled: Information Theoretic
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diagnostic pathways. Core research tasks include planning, conducting and publishing epidemiological studies using large-scale observational data, primarily register-based, with a focus on effects of ADHD
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As research assistant, your primary tasks are full time laboratory work and data analysis. You contribute to the development of the department through research of high international quality. In your
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Postdoc in Decoding Biological Nitrification Inhibition (BNI) in Cereals: Integrating Metabolomic...
An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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, and service areas, and seeks to develop a quantitative measurement approach including comprehensive validation strategies. For more information, please see the complete job advertisement by clicking
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An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data