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, motivated, and creative researcher with a strong background in deep learning architectures, and image analysis and interested in working on interdisciplinary project deeply rooted in biology, and with a
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Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
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key regulators of inflammation and tissue remodeling in gut and skin diseases. • Apply and refine AI/ML methods, including deep learning, neural networks, and interpretable models (e.g., SHAP, BioMapAI
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simulations with deep learning neural networks and swarm robots, virtual reality experiments, animal communication research, and more. In a range of projects, we show that languages can effectively be seen as
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-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated evidence of excellent programmin g, collaboration
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are learning more and more how it relates to human health and disease. The new generation of deep metagenomic sequencing, consists in simultaneous sequencing of multiple microbial genomes at once and has
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Computer Science, Artificial Intelligence, Machine Learning, or a related field; Strong background in image processing, computer vision, and deep learning; Excellent programming skills (Python preferred), with
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to advancing understanding of brain circuitry. The laboratory currently has multiple collaborative R01 funded grants from NIMH and NIDA with Drs. Hearing, John Mantsch (Medical College of Wisconsin), and Robert
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on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and application of machine
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM