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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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: www.jura.ku.dk . The Faculty actively supports efforts to learn Danish. Qualification requirements Employment as a Postdoc requires academic qualifications at PhD level. More information on careers at UCPH and the
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
collaboration between the Department of Electrical and Computer Engineering and the Novo Nordisk Foundation CO2 research center, Aarhus University, we aim to address this opportunity by developing digital twins
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
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developed algorithms with the designed hardware in the best way. Document design specifications, and design trade-offs clearly. Qualifications Applicants should hold a PhD in electronics, computer engineering
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in ion transport and (2) using machine learning methods to design protein binders. The incoming postdoc will have the opportunity to mould the project to a significant degree. The candidate should have
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The Department of Agroecology at Aarhus University, Denmark, is offering a postdoctoral position in machine learning for advanced peatland mapping, starting 01-12-2025 or as soon as possible