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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
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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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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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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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) Emory University (Atlanta, USA) Further analysis might be required, based on the development of research project. MAIN SUPERVISOR: Prof. Bart Vanrumste (e-mail: bart.vanrumste@kuleuven.be )Due to the high
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manufacturing machines (looms, bobbinfeeders, ...) under dynamic conditions. Such simulations are very challenging due to the use of diverse materials (natural and synthetic fibers, yarns and fabrics) which
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, followed by post-plasma catalysis into C2H4; (2) fully coupled modelling (i.e., plasma fluid dynamics and plasma + catalyst surface chemistry) for plasma-based CH4 conversion with post-plasma catalysis
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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
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on discovery of next-generation immunotherapy for kidney cancer through different state-of-the-art approaches integrating multi-omics and translational immunology. You must hold a MD or MSc/Masters’ degree in