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science, computational sciences, or mathematics is preferred. The successful candidate will need to have completed all PhD requirements by their start date. Knowledge required includes machine learning and statistical
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, and evaluation The ideal candidate will lead their own project, and also collaborate with and support 1-2 PhD students on their projects. The ideal candidate will also be interested in learning
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quantification. You will research and develop frontier analysis methods, including machine-learning approaches, for the accelerated interpretation of x-ray scattering data. You will develop multi-modal data
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along with experience in applying Artificial Intelligence (AI)/Machine Learning tools, OR a PhD in AI/Machine Learning with experience in plasma physics or other strongly coupled physical systems
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dependent upon years of experience (NIH Post Doc pay scale) Type of Position Student Position Time Status Full-Time Required Education PhD, DrPH, or other doctoral degree in public health, sociology
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, operation, control, and security. Required Knowledge, Skills, and Abilities: A PhD. with a broad knowledge of electric power systems. Demonstrated background in artificial intelligence (AI)/machine learning
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, bioinformatic and machine learning approaches to identify biomarkers and molecular pathways underlying cognitive decline, Alzheimer’s disease, cardiometabolic diseases (e.g., type 2 diabetes, cardiovascular
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single-cell transcriptomic data. The candidate should be proficient in, or highly motivated to learn cancer data science, machine learning, and high throughput sequencing analysis. Successful applicants
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Physics, Electrical Engineering, Material Science or closely related field in hand
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production; to further develop and calibrate algorithms for tagging boosted Higgs boson decays; to explore novel applications of machine learning to particle physics data analysis and detector hardware; and to