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screening (Ulrike Haug), prevention and implementation science (Hajo Zeeb, Daniela Fuhr), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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Electrical Engineering and Computer Science Nanotechnology/Devices (more...) Electrical and Computer Engineering Health Tech Microelectronics BioNanotechnology Artificial Intelligence, Machine Learning and
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Human Genome-Phenome Archive. The position will also be connected to a vibrant local ecosystem for data science and machine learning. Your Tasks The research group of Dr. Brian Clarke is looking for a
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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The Institute for Infection Prevention and Control (IPC, Head Prof. Dr. Philipp Henneke) is looking as soon as possible for a Bioinformatician (m/f/d, PhD) with focus on the analysis of large
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for a PhD student to join our team and help us make exciting new advances in applications of machine learning (ML) strategies for analyzing X-ray and neutron scattering data! You will be working in a