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Field
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of Stellar Systems' research group led by Prof. Glenn van de Ven. Our ideal candidate has a solid background in computational astrophysics, strong track record in software development, and keen interest in
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-of- the-art spectroscopy requires novel optimisation algorithms and efficient programming. • You can show a proven track record in object-oriented programming as necessary in larger software development teams
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and development in designing and programming fied programmable gate arrays (FPGAs) for accelerating artificial intelligence (AI) algorithms. contributing, administrating and reporting in (inter
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learning, optimization algorithms, and interoperability frameworks for optimal energy management across Europe. KTH leads technological landscape analysis, multi-energy investment planning tool development
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multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant
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We are the research groups “Tectonics and Sedimentary Systems” of the Institute of Geological Sciences at the Freie Universität Berlin under the direction of Prof. Dr. Alexander Webb and Prof. Dr
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develop new communication theory and signal processing algorithms. The goal will be to develop theory, algorithms, and network architectural concepts to deliver ubiquitous network services across the globe
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the mathematical foundations of these fields, e.g., designing innovative algorithms and control strategies, as well as the development of technical solutions to adapt these new methods to applications in the areas
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of Information Technology. We investigate and develop methods, theories, and technologies for digitalized data and the information centric society. Strong mathematical, algorithmic, and computational thinking and
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cutting-edge image-based multiphysics modelling of cardiovascular fluid dynamics and device-tissue interactions. You will work with clinical and experimental data to develop innovative geometric deep