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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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machine learning processing of the spectroscopic data • The optical design and development of novel custom spectroscopic sensors benefitting from freeform optics. • Integration of the in-situ
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and success evaluation of prototype implementations. More generally, the PhD thesis is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning
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particular focus on the application of machine learning and big data methods. The position involves producing high-quality, peer-reviewed research suitable for publication in leading international journals, as
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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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courses on advanced semiconductor technologies Design pathfinding PDKs as learning assets Interuniversity research programs across Europe 🔬 Nano IC-related PhD topics include: Machine-learning for epitaxy