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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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, 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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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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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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(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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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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for environmental epidemiology (Epi, survival, sf, gstat, mgcv) and causal inference (dagitty, MatchIt), as well as contributing to reproducible, scalable data pipelines. Machine learning integration: Exploring ML