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Field
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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to the topic, including food safety, microbiology, computational biology, machine learning, artificial intelligence, data science, or other related scientific fields. Familiarity with data-driven
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline
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expertise in machine learning or computational modelling who are eager to advance conceptual innovation toward practical industrial deployment. Qualifications PhD in Computer Science, Machine Learning
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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-body quantum geometry; altermagnetism; cavity quantum science; quantum non-equilibrium processes; Casimir physics , Non-equilibrium quantum physics , Physics-informed machine learning , Quantum chaos
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=50415 Requirements Research FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications Advanced skills in Machine Learning and Artificial Intelligence Proficiency in spoken and written English
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for the hydrogen workforce. Project Focus This Postdoc Research Aims To: Examine how remote labs and digital learning environments can accelerate skill development and reduce time-to-job for hydrogen professionals