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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
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related to staff position within a Research Infrastructure? No Offer Description Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands
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work with deep learning frameworks (e.g., pyTorch, TensorFlow) Proven publication record in relevant fields Familiarity with high-performance computing environments Familiarity with using and adapting
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Intelligence group and the Deep Data Mining group at the Department of Computing Science, collaborating with researchers in, e.g., data science, machine learning, and responsible AI. More info about the research
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connectivity operate as one. You will join an interdisciplinary and collaborative research environment that values creativity, initiative, and experimentation. We combine deep systems research with hands