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of these programming languages is welcome)- Machine learning experience is valued- Multiphase flow and heat/mass transfer.- Programming and algorithm development.- Excellent communication skills and fluency in written
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. The successful candidate will be at the forefront of integrating advanced machine learning techniques with biomolecular simulation and modeling to address complex biological problems. This position offers a unique
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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a merit.The research project is experimental in nature and you should be motivated to work in the lab, learn new techniques and have the ability to take initiative. You should also be organized and
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processing techniques, supported by artificial intelligence and machine learning algorithms, to streamline the interpretation of spectroscopic data. The research will be carried out in close collaboration
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Do you want to contribute to improving human health? Division Do you want to contribute as a post-doctoral researcher analyzing machine-learning approaches to detect and diagnose breast cancer in
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evolution.Candidate profile: Ph.D. degree in signal processing, machine learning or applied mathematics or related fields.To apply: If interested, please send your application including an academic CV and a motivation
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,- Machine vision and deep learning for dynamic scene understanding.Moreover, the candidate will have the opportunity to implement and evaluate the proposed algorithms and schemeson autonomous vehicles
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Design for FPGA/SoC-FPGA and hands-on experience in prototyping and implementations Implementation of DNN/CNN in FPGAs and/or other accelerators/systems Machine Learning/Artificial Intelligence (PyTorch
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for this development, but will work in a team of other PhD candidates, postdocs and technicians that will assist. Finally, the full 0.7-MV prototype will be developed along similar lines and with all lessons learned