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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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reliability of autonomous Guidance, Navigation, and Control (GNC) algorithms when subjected to realistic stimuli. This research stems from the legacy of the ERC-funded EXTREMA and MUR-funded COSMICA projects
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The School of Mechanical and Aerospace Engineering (MAE) is seeking to hire a Research Associate to work on the
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on experience and qualifications). More information about this position is available on https://uol.de/en/job/research-associate-foundation-models-for-hearing-aid-processing-819 The position is embedded within
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testing of model-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs
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computational algorithm and formulation for generating optimised structure and toolpaths while incorporating the manufacturing constraints. Candidate should have a PhD degree in Mechanical Engineering and strong
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programme Reference Number AE2025-0602 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0602
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of quantum algorithms for hybrid quantum simulators. Applicants should have a PhD in Physics, Chemistry, Computer Science, or a closely related field. To apply, a CV, a brief statement of research interests
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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Operations Manager to lead and monitor operations for continuous algorithm improvement and validation. This will involve guiding the development of data management systems and establishing data assessment