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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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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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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
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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, Electrical Engineering, Aerospace Engineering or a related field, with a focus on Robotic Perception and learning based methods Demonstrated expertise in at least one of the following areas: Machine Learning
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related field. The ideal candidate should have knowledge and/or experience in one or several of the following areas: Artificial Intelligence/Machine Learning algorithms and architectures Cybersecurity