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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
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for the development of gait assessment FMs; A FM for assessing FOG severity; A FMs-based application for FOG severity assessment. INDICATIVE PLANNED SECONDMENTS Radboud University Medical Center (Nijmegen, Netherlands
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13 Dec 2025 Job Information Organisation/Company KU LEUVEN Research Field Architecture » Design Engineering » Communication engineering Engineering » Computer engineering Engineering » Electrical
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potential mechanisms required for selective autophagy and how the autophagy machinery links up with cargoes including aggregates.In this project, the PhD candidate will build on a strong foundation of
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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and pathways between multiple environmental and occupational exposures – also known as the exposome – and (immune) disease/ health. We are looking for a PhD candidate to study the exposome-immunome
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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18 Nov 2025 Job Information Organisation/Company KU LEUVEN Research Field Mathematics » Computational mathematics Researcher Profile First Stage Researcher (R1) Country Belgium Application Deadline 30 Apr 2026 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 1...
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to study the binary stellar hearts and the core-collapse process that form them. PhD project 1 – Investigating the Explosion and Implosion Signatures of Binary StarsThe aim of this PhD project is to
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning