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quantitative field. Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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in resulting companies, etc. The work will comprise machine learning research for analysing large-scale clinical data, including time-series physiological data, blood test data, medications
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, or methodologies in bioengineering. This search has a particular focus in immunology, neuroscience, and/or computational science/machine learning. That said, we give high priority to the overall originality and
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, machine learning model applications, and real-time applications Opportunities to learn more about systems neuroscience and neuroengineering One on one mentorship with graduate students and postdocs
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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a skilled Data Scientist with a strong foundation in genomic biostatistics to join our team. This role involves leveraging advanced statistical methods and machine learning techniques to analyze
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the synaptic wiring diagram, or connectome, of whole brains, designing, building and using machine learning approaches to computer vision as applied to volume electron microscopy of whole brains. The ideal
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. Describe a deep learning project you have executed. Projects in computer vision for microscopy image analysis are especially relevant. Include a link to a code repository if possible. If you contributed to a