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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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/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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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
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. Significant experience of developing deep learning methods using computational frameworks such as PyTorch, TensorFlow etc. Experience of working with molecular questions in the biosciences and applying AI
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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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Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands-on engineering. Historically it has been instrumental in many areas, from
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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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strong computational–experimental feedback loop central to the project. Subject description Recent breakthroughs in deep learning–powered protein design, recognized by the 2024 Nobel Prize in Chemistry
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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