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Field
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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hands-on experience with Python and ML libraries (PyTorch/TensorFlow) experience with signal/image processing or computer vision for video strong programming and data engineering skills Desirable
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of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences). Who we are looking for An enthusiastic, self-motivated, resourceful
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis
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, methodologies, techniques, and principles. Ability to apply knowledge of the subject area. Technology Skills Demonstrate proficiency using computer applications, online resources, and other technologies
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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after employment. Required selection criteria You must have a relevant Master's degree in Reliability, Availability, Maintainability, and Safety (RAMS) or Cybernetics or Computer/Information Science with
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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the life sciences and related disciplines (e.g. biomedical sciences, biology, biochemistry, physics, chemistry, psychology, computer sciences, engineering) Acceptance by an authorised academic supervisor