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your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research
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activities. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
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or triggers for the ATLAS experiment would be highly desirable, as would experience with deep learning and concurrent programming The post will work in the first instance the High Energy Physics Group
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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motivated individual with experience in deep learning and a PhD in computer science, electrical engineering, biomedical engineering, biomedical informatics, biostatistics or a related discipline. Required
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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candidates will have experience in artificial intelligence, deep learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data
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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram
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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