22 computer-science-"Katholieke-Universiteit-Leuven" scholarships at Forschungszentrum Jülich in Germany
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about new scientific endeavors with interdisciplinary approaches. For this you have: A completed university degree (Master or equivalent) in data science, computer science, mathematics, materials science
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completed scientific university (Master) studies in electrical engineering, informatics or comparable Strong mathematical background Knowledge and experience in programming (Python, C, C++) Knowledge of state
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well as in the form of publications in relevant journals Your Profile: Excellent Masters degree in electrical engineering or comparable Programming skills in Python and C++ Fluent written and spoken English
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/electronic engineering, computer science, computer engineering, physics, and related fields. For IC projects a strong electronics background, with experience in design and simulation of analog, digital
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. Additionally, enjoyment of teamwork is an important requirement. Masters degree in electrical/electronic engineering, computer science, computer engineering, physics, and related fields. For IC projects a strong
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, simulation of the vacuum conditions and heat management during operation, estimation of the pumping capacities including engineering of the pumping stations, gauges and valves, define the operating and system
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: You will strengthen the data science and machine learning
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data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s degree in Engineering, Computer Science, Physics
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Your Job: The MOI-V Manchot Graduate School`s research programme focuses on the functional characterisation of molecules that are essential for infections with a wide variety of pathogens
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, including JUPITER, Europe`s first exascale computer Prepare, process and publish datasets and benchmarks for self-supervised learning in science Engage in national and international ML/DL communities, most