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epidemiology and/or statistical genetics as per 1 June 2026 or as soon as possible thereafter. The position is a fixed-term full-time position for 2 years. The National Centre for Register-based Research
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the Machine Learning and Artificial Intelligence. Solid mathematical and analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Experience in programming
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Science including quantitative and qualitative consumer research methodologies, and experience in designing consumer studies in and out-side Denmark. Solid experience in statistical analysis are preferred
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of Chemistry, and the group of Prof. Hobolth at the Department of Mathematics. A second postdoc with expertise in stochastic processes and statistical methods will be part of the project and you are expected
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ecological data collection. The positions focus on improving detection and classification performance of deep learning models applied to millions of images collected in European monitoring programs. Key
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limited to one year with the possibility of extension. The research assistant position may potentially lead to the opportunity to begin a PhD position based on the candidate’s career stage and wishes. We
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CFD workflows and Lagrangian particle/cell tracking to extracting actionable insights with statistical learning and AI/ML—ultimately enabling more robust scale‑up, smarter process control, and faster
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(programming, digital signal processing, modelling, statistics) is sought. The project involves collaboration with Prof. Marco Steinhauser (University of Eichstätt-Ingolstadt, Germany) and Dr. Ramakrishna
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(programming, digital signal processing, statistics) is sought. Qualifications Ideally, applicants for the position should satisfy the following requirements: PhD degree in psychology, cognitive (neuro)science
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needs. Participate in the Urban M2O dissemination activities and contribute to the preparation of the project scientific reports You are expected to have, or to rapidly develop, good programming skills