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been previously unavailable and could benefit the broader biomedical community, including the pharmaceutical industry. Qualifications Master-level degree in electrical engineering, physics, mechanical
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from all backgrounds to join our community. The Nonlinear Systems and Control group (https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/nonlinear-systems-and-control ) at Aalto
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the salary range £43,863-£47,223 per annum. The post is available from 01 March 2026 for up to 24 months. The post is based in the Department of Electrical and Electronic Engineering at Imperial College
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of control theory and optimization. Knowledge of partial differential equations. Have a strong coding ability Research Associate: Hold a PhD in Engineering, Mathematics or a closely related discipline, or
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alto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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Applications are invited for a fully funded fixed-term position at the Research Associate (PostDoc) level in de-risking cirrus modification. Cirrus cloud modification (CCM) could in-theory mitigate
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models with a practical experimental platform. FTE: 1 (35 hours/week) Term: Fixed (18 months) The Centre for Ultrasonic Engineering (CUE) group of the Institute for Sensors, Signals and Communications
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour