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learning, and signal processing applied to conduct high quality research with a particular focus on neuropsychiatric and cardiovascular diseases. Working in a large team of 15 PhD students, several postdocs
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
, and their coupling, using machine learning. The postdoc will be expected to collaborate with other postdocs at Princeton and with other members of the M2LInES project across multiple institutions.In
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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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Vacancies Postdoc position on AI-based treatment selection in aneurysm patients Key takeaways Are you excited about applying AI to solve real clinical challenges? As a postdoctoral researcher in the
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chemical discoveries for renewable energy, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing
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https://www.academictransfer.com/en/jobs/354348/postdoc-position-on-ai-based-tr… Requirements Specific Requirements PhD in a relevant field (e.g., medical imaging, computer vision, AI) Expertise in
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
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vision, machine learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100
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student) and learning environment (e.g., online versus offline, use of simulation). Presently, the intended focus of the postdoc will be on data management, analyses, and dissemination rather than data