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.) You will work with one of the most comprehensive multimodal datasets available, enabling research at the frontier of data-driven biology. What you’ll do Develop and train large-scale machine learning
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: machine learning, data analysis, statistical modelling, explainable AI, computational methods for large-scale data, and analysis of biomedical or population-based datasets. An interest in applications in
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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methods for understanding biological form, function, and evolution. The project combines computer vision, machine learning, genomics, and biomechanics, and involves large-scale multimodal datasets including
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vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics. Experience in deep learning, computer vision, or neural
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· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively
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is enabling major advances in clinical pathology and cancer diagnostics. Today’s AI methods require large amounts of data with a detailed ground truth annotation that the AI system can learn from. In
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, proteomics, long-read sequencing). Familiarity with machine learning approaches, particularly artificial neural networks, and their application to biological data. Experience with workflow management systems
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an