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and implantable sensors o Microfluidic-based electrochemical platforms o Data-driven sensor signal processing Fluent English proficiency (B2/C1 level or higher). Strong analytical, problem-solving
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, Materials Science, Chemical Engineering, Nanotechnology, or related fields. Proven experience in one or more of the following areas: o Electrochemical sensors and devices o Energy storage/conversion systems
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on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.The
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older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early
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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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potentially pose a risk during the proximity operations a kick stage would undertake, for example, condensing on sensitive surfaces such as solar arrays and optical or other sensors. This collaboration between
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analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and