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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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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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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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you to apply. The opportunity to work on a challenging and advanced research topic, in a dynamic academic environment, to acquire research and project management skills, to network with leading
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management, and machine learning approaches for process monitoring and control For this function, our Brussels Humanities, Sciences & Engineering Campus (Elsene) will serve as your home base.
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or Nextflow A willingness to learn and apply machine learning approaches Offer A doctoral scholarship for a period of 1 year to start, with the possibility of renewal for a further three-year period after
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) inconsortium/institutional seminars and national/international symposiums or conferences. Eager to carry out visitations (if required) to other laboratories (within Belgium or abroad) for learning new techniques
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eager to learn, resilient and motivated to successfully complete a multi-year research project. You demonstrate ownership of your learning and can handle feedback. You are able to prioritize and work
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with large-scale data analysis, such as genomics or transcriptomics data Experience with a workflow management system such as Snakemake or Nextflow A willingness to learn and apply machine learning
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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research