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intelligence and machine learning across several domains of physics and astronomy. The successful candidates will develop an integrated image-processing pipeline and apply it to three distinct problems: rare
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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, and additional expertise in mechanical testing and materials characterization will be considered a strong advantage. Required Qualifications PhD in Materials Science, Mechanical Engineering, Additive
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modeling approaches will be considered a strong advantage. Required Qualifications PhD in Chemical Engineering, Materials Science, Mechanical Engineering, Chemistry, Electrochemical Engineering, or a closely
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Do you want to combine high-throughput directed evolution with machine-learning analysis of deep sequencing data to engineer better antibodies? The Sormanni Lab in the Department of Chemical
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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Generation, digital double, human-like avatars. Profile PhD in Computer Vision Background in CS Research experience in gen AI, deep learning Strong computational and analytical skills, as well as experience
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. There will be close collaboration with policymakers to apply empirical safety research for AI regulation and governance. You should possess a PhD or DPhil (or near completion of) in Machine Learning. You