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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track
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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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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine
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Sciences Faculty of Mathematics Further information Hochschule Offenburg Department of Electrical Engineering, Medical Engineering and Computer Science: Institute for Machine Learning and Analytics Institute
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-homepage Implementation of empirical studies with the social robot Advising students on technical issues You bring with you: B.Sc.-degree in computer science, computer linguistics, data science and AI
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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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collaborators at the Fritz-Haber-Institute (MPG) you will help to explore the microscopic processes behind electron, lattice, and spin interactions in magnetic van-der-Waals materials. During this, you will learn