145 computer-programmer-"Diamond-Light-Source"-"Diamond-Light-Source" positions at Technical University of Munich in Germany
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technical field, such as mechanical engineering, computer science, transportation systems, or civil/environmental engineering with a focus on traffic engineering, with very good grades. You bring solid
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national grants. You will plan, design, and manufacture our sensor to be used in clinical trials, work on data acquisition and data analysis. The results of your work will not only accelerate your scientific
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engineering, computer science or electrical engineering, with good grades. Experience in scientific work, project proposal writing and team leadership are part of your repertoire. In addition, you are
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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. Contact Prof. Holger Boche, Technical University of Munich, School of Computation, Information and Technology, Chair of Theoretical Information Technology, Theresienstrasse 90, 80333 Munich. https
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for benchmarking, HPDA/HPC support, and education (e.g. MOOCs, organizing workshops, facilitating community building). Requirements: Completed university degree in computer science or applied mathematics, remote
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expertise from EO, robotics, computer vision and HPC/HPDA support. DLR also started strategic cooperation with Leibniz Supercomputing Centre, e.g. through recently signed cooperation agreement “Terra Byte
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infrastructure for research Excellent training and career support opportunities (courses, personal coaching, ...) Your qualifications Master’s degree in Computer Science or a similar field Good theoretical
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random