34 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Antwerp
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(100%) PhD in the field of Molecular and Computational Neurogenomics Position We are a highly motivated international team of researchers at the Molecular Neurogenomics group (Jordanova Lab) and the
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support tasks for the Bittremieux Lab, such as assisting in practical teaching sessions and supervising Bachelor and Master students. Profile You hold a Master degree in Computer Science, Machine Learning
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line with the University of Antwerp’s educational vision . What we offer We offer a PhD position for a period of two years. Following a positive evaluation and sufficiently available funds, the position
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-time (100%) PhD position on Optical Coherence Tomography to assess the structural and mechanical properties of the eardrum and inner ear under the supervision of Professor Sam Van der Jeught (InViLab
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-time (100%) PhD position on Optical Coherence Tomography to determine eardrum stiffness in-vivo under the supervision of Professor Sam Van der Jeught (University of Antwerp) and Dr. Manuel Marques
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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 approaches What we offer As
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set a course for the future – a future that you can help to shape. The EMAT research group at the Faculty of Science (University of Antwerp) is seeking to fill a PhD position on the Crystal structure
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(involving, e.g., computer programmers and data scientists, military startups, technology firms) and how it leads or has led to new and compounding forms of civilian harm in Ukraine and Gaza. It is co-led by
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mortality rate. This project will involve a comprehensive approach combining bacterial whole genome sequencing, genomics based bio-informatics, chemical analyses of bacterial compounds and immunological
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talented PhD students to develop and benchmark cutting-edge super-resolution imaging technologies. The project aims to establish a comprehensive approach for ultra-high-content, multimodal imaging