225 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Technical University of Munich in Germany
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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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very good master's degree in a topic-related field (computer science, automotive engineering, electrical engineering) Experience in scientific work, project acquisition as well as management experience
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08.09.2021, Academic staff The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%, 3
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own statistical and computational challenges, and standard tools often cannot be applied. The purpose of the position and goal of the project is to develop and apply bioinformatic tools for the analysis
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computers and the ones using them. We are working on a visionary and large-scale ERC Consolidator Grant Project and are a part of the Munich Quantum Valley-Initiative (https://www.munich-quantum-valley.de
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21.12.2021, Academic staff The Department of Computer Science, Technical University of Munich, has a vacancy for a PhD candidate/researcher position in the area of efficient algorithms. The position
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programme „Information Engineering“ in Heilbronn which is taught in English. The Faculty of Informatics at the Technical University of Munich intends will fill a position at the earliest as Scientific
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on the nanoscale (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). You will also supervise one PhD student who will work on a complementary topic guaranteeing quick output and an ideal
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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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Master’s degree in physics, chemistry, materials science, chemical engineering, or a related field who are excited about applying machine learning and data science to real-world materials challenges