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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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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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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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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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. 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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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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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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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
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machine learning, generative models, or data science methods; Engaging with public outreach activities and supporting MSc and PhD students’ supervision as requested. Job requirements Essential Requirements