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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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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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knowledge of Microsoft Word, Excel and PowerPoint. You have experience in, or are willing to learn MATLAB. You hold the FELASA B certificate, or international equivalent or you are willing to follow a
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international workshops and conferences, presenting and discussing your research globally. Teach and contribute: Provide support for teaching activities and teaching innovation. Build up and apply skills: Build
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, molecular biology, or related disciplines FELASA certification Experience with or highly interested in experimental immunology (e.g. in vivo models, flow cytometry, imaging) Motivation to learn and apply
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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
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within