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UPOs PhD enrolment: Université Paris Cité DC15: Hybrid machine learning models for data-driven bioprocess optimisation PhD enrolment: University of Padua Eligibility Requirements: Doctoral Candidates
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learning, optimisation, system modelling, or related quantitative methods, as acquired through master’s level coursework and project work. Familiarity with data driven modelling approaches relevant to energy
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods, machine learning tools, and simulation techniques. If you thrive
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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student to work within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). The project will leverage Large Language Models (LLMs) as active software agents to help automate
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM, data-driven sensor fusion) is a plus. Underwater or field robotics experience