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prosthetics. The project will be supervised by Prof. Sarah Cartmell, Prof. Julian Yates, and Dr. Jose R. Aguilar Cosme at the University of Manchester. While prosthetic materials continue to evolve, current
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Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical engineering, computational neuroscience
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Europe | 20 days ago
Lisboa (Iscte)Research Field: Electrical Engineering, Physics, Computer Science, Applied Physics/OpticsResearcher Profile: First Stage Researcher (R1)Application Deadline: 26 August 2025 – 23:59 (Lisbon
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related discipline. To apply, please contact the supervisor, Prof Foster - david.foster@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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computing facilities and membership in the multidisciplinary Control & Power Group, which spans control engineering, energy systems and sustainability. Sector-leading salary and remuneration package
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academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 12 days ago
program, funded by the German Science Foundation. The PhD position will be supervised by Prof. Werner Becker at MPE Garching. Our offer A versatile, challenging and autonomous job in a dynamic and
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education. You must meet the requirements for admission to the doctoral programme in Materials Science and Engineering, https://www.ntnu.edu/studies/phmt . Your academic degree must contain appropriate
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description