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infinite-dimensional (e.g., space-dependent) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive
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environmental NCD risk score using statistical and ML methods. Prospective phase: Use wearable environmental and physiological sensors to track exposures and health parameters in healthy volunteers, translating
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fermentation and downstream recovery process and the impact of important process parameters (e.g., pressure, mixing) in industrially relevant and scalable fermentation equipment. Beyond the specific expertise
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autonomous driving. Your profile Master's degree in Computer Science, Artificial Intelligence, Robotics, or related field Strong background in machine learning, deep learning, or computer vision Experience
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26 Oct 2025 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Computer engineering Engineering » Electrical engineering Engineering » Electronic engineering Engineering
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» Science communication Computer science » Other Engineering » Design engineering Technology » Computer technology Technology » Future technology Arts » Visual arts Computer science » Informatics Researcher
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in Mechanical or Electrical Engineering, Computer Science, or a related field. Fluency in at least one common computer programming language is required. English fluency is expected. The candidate must
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position on GW data analysis using machine learning (ML) with expected starting date February 2026. The position focuses on using neural posterior estimation for tackling issues related to the analysis
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) estimators on an FPGA, as real-time input for the control system · Development of state space and system identification models of the DED-LB process, with emphasis on composition effects in the melt bath
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summertime in the northern mid-latitudes, including the North Sea. For example, preliminary studies estimated a 30-year energy production decrease of 5% in summer and 2.5% in autumn. This introduces