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Postdocs in Generative Machine Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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? Information We are seeking a motivated postdoctoral researcher to develop computational frameworks that integrate quantum-mechanical simulations (DFT, many-body methods) with modern machine learning approaches
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candidate with an interest in the application of machine learning to optics, touching upon aspects of computational electromagnetism and inverse design. Information about the project and division Structured
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and data structures for AI kernels; Scalable systems for machine learning (training, inference, edge); HW-SW co-design for computer vision on novel architectures; Desired skills Advanced knowledge in
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dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and passion for research in the field of sustainable
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the application of machine learning to optics, touching upon aspects of computational electromagnetism and inverse design. Information about the project and division Structured optical materials, such as
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. You will primarily be responsible for building a machine learning model and performing computational genomics or quantitative genetics on new sequencing data. Your goal will be to develop a system that
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. Publish and present findings in international journals and conferences. Supervise and mentor junior researchers and students. Profile PhD in Computer Science, Biomedical Engineering, Data Science, or a
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large genomics data and later incorporating the testing on known unwanted linkages. You will primarily be responsible for building a machine learning model and performing computational genomics