76 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at Nature Careers in Germany
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on healthcare, research, and teaching, we bear a unique responsibility – ideally with you on board! The position is based at the Institute of Medical Informatics within the research group Machine Learning
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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or machine learning, proficiency in deep learning techniques (CNN, VIT, diffusion, GAN) Good understanding of the mathematical foundations of machine learning Mastering python and related AI software
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Postdoctoral researcher (m/f/d) in the field of physics, physical engineering, technical computer sc
To strengthen our team in the division “Acoustic and Electromagnetic Methods" in Berlin-Steglitz, starting 01.01.2026, we are looking for a Postdoctoral researcher (m/f/d) in the field of physics, physical engineering, technical computer science, electrical engineering or similar Salary group...
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Professor (W3 / W2 with Tenure Track to W3) for Materials and/or devices for Photonics and Quantum T
quantum technologies. This research can be complemented by digital methods of process simulation and optimization, as well as machine learning. Requirements include an outstanding PhD in materials science
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), proteomics (LC-MS/MS), (epi)genomic data processing, multi-omics integration, machine learning approaches for high-dimensional data, confocal / two-photon imaging, tissue clearing and light-sheet microscopy
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skills in data science, advanced AI interface development and machine learning?to apply climate change and other relevant data to real-world problems exchange and coordination with project partners Your
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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outstanding researchers with a strong track record of advancing the state of the art in machine learning and potentially its application to biology and biomedicine, with the ambition to build an internationally