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(such as heart disease, diabetes, and cancer) using, for example, data from registries and/or biobanks. The research will be performed in close collaboration with Center for Clinical Data Science (CLINDA
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, robust, and trustworthy robotic technologies. Your research will span core challenges such as robot control, decision-making under uncertainty, multimodal information fusion, and foundational models
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Technology, Computer Science, Artificial Intelligence, Data Science, Robotics or any other relevant field, at the time of admission into this PhD program. Students currently enrolled in Master’s programs can apply, provided
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theoretically. Further information can be obtained in the department’s qualification guidelines. Successful candidates will be expected to teach and supervise students in our business administration programs
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about the different pension schemes, please read more here: https://ism.ku.dk/salary-tax-pension/pension/ If you consider applying from abroad, you may find useful information on how it is to work in
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agreement with the Confederation of Professional Associations. The allowance will be agreed with the relevant union. Further information Further information may be obtained from Head of Department, Professor
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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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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, as well as other sustainability relevant endeavours Integrating advanced machine learning methods in thermodynamics for computer-aided property predictions, molecular and product design and
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum