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microscopy methods (darkfield, photothermal, ultrafast, interferometric), electron microscopy, machine learning and other advanced statistical methods. Required Application Materials Cover letter, curriculum
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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to an advanced Laboratory Directed Research and Development (LDRD) project, "Machine Learning Steered EXAFS Fitting for Autonomous XAS Analysis," aimed at revolutionizing real-time analysis of X-ray Absorption
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and/or cutting edges machine learning techniques to make foundational discoveries in reproductive medicine. The annual salary for this full-time position starts at $76,383, dependent upon skills and
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, spectropolarimetric inversion techniques, and machine-learning–based approaches, for the physical interpretation of solar images and spectral profiles. Special consideration will be given to applicants with experience
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and Cambridge as well as worldwide. This is a unique opportunity with potential for the student to work directly with the PI as well as senior postdoctoral fellows, graduate students, and medical
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Caribbean populations of African descent. Most mechanistic insights derive from non-representative cohorts, limiting biomarker discovery and therapeutic precision. Recent multi-omic and machine learning
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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, and MR spectroscopic imaging using machine learning; candidates with experience in these areas are encouraged to apply. PREFERRED QUALIFICATIONS: APPLICATION PROCEDURE: Apply online at https
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and application of AI and machine learning methods; (iii) a good track record of research and publication in top peer-reviewed scientific journals in the area of general medicine, cardio