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applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine
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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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the progression of ARDS in intensive care patients with sepsis. To enable this, we will develop information-theoretic machine learning methods to determine which protein interactions are driving disease progression
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probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify
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with such models. Experience with machine learning methods applied to biological data. Familiarity with large language model APIs and frameworks (e.g., Claude/Anthropic API, OpenAI API, LangChain
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of physics‑informed control of mobile manipulators, data collection from real and simulated machines, and model development and testing in simulated environments. The project offers close collaboration with
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks - Documented several years of experience in systems development with Python and
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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: Applied mathematics; Machine Learning; Mathematical Modelling Appl Deadline: 2026/03/24 10:59 PM UnitedKingdomTime (posted 2026/03/18 04:00 AM UnitedKingdomTime, listed until 2026/04/01 04:59 AM