674 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions in Denmark
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. Please refer to http://mbg.au.dk/ for further information about The Department of Molecular Biology and Genetics and to https://nat.au.dk/ and http://www.au.dk/ for information on Faculty of Natural
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support schools, day-care centres, and youth education in creating conditions for engagement, learning, and mental health. The role of psychology in policy and governance, including how psychological
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protocol on certain terms of employment of academic staff at universities (only in Danish). Where to apply Website https://aau.varbi.com/en/what:job/jobID:899091/type:job/where:39/apply:1 Requirements
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computational biology with a strong focus on development of computational and statistical methods, particularly within machine learning and artificial intelligence. The applicant must have earned a PhD degree and
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Marie-Skłodowska-Curie Action (MSCA) grant on WindConnect project. The project The WindConnect (wind farm control and integration in sector-coupled power systems https://cordis.europa.eu/project/id
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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the ability to perform complex data analyses. Has experience with implementing computer-based experiments as well as field experiments. Has professional proficiency in English, both written and spoken
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Dynamics, Bioprocess engineering, Data Science, Machine Learning, Computational Chemistry Offer Description MSCA Doctoral Network machinE LEarning for inteGrated and multi-parAmetric eNzyme and bioproCess
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staff office (ISO) https://www.sdu.dk/en/om-sdu/job-sdu/international-staff For the right candidate, there will be possibilities to influence the project and develop new project ideas within the project
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support