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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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need to match every topic or method listed below. We value deep expertise in at least one area, intellectual range, scientific ambition, and the ability to collaborate across computer science and the
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for individuals, organizations or society. You will be part of the SAIL research group, whose research activities focus on AI-based modeling of complex systems, uncertainty management in deep learning, visual
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, Chemistry, Physics, Applied Mathematics, Materials Science, Chemical Engineering, or a related technical field. Demonstrated research experience in machine learning or deep learning for scientific
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs). *Strong programming proficiency in Python and familiarity with standard data science and machine learning
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 3 months ago
research on population coding and Alzheimer’s disease Key research methods: fMRI (including 7T), MEG, virtual reality (VR), a wide variety of cognitive tasks, psychophysics, machine learning, deep neural
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results-oriented team player. Ideal profile includes following competencies and experience: Strong programming skills, deep statistical knowledge and a proven track record with machine learning Solid
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heterogeneity in cancer, inflammation, and tissue senescence. • Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial
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: ● Applicants must have a PhD in Computer Science or related field, with no more than five years post receipt of the PhD. ● Experience in one or more ML domains, such as deep learning, reinforcement learning