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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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-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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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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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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/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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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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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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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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learning, and responsible AI. More info about the research groups: https://www.umu.se/en/department-of-computing-science/research/research-groups/ . Qualifications To be appointed under the postdoctoral