89 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred
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and attention to detail; Proven technical and analytical skills; Ability to troubleshoot an experiment as necessary. Proficiency with a computer; Knowledge of math and statistics, experience with PRISM
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experience. Research background in decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow
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Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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. She is also a faculty member in the Biophysics Program and a Faculty Fellow of the Sarafan ChEM-H (Chemistry, Engineering and Medicine for Human Health) Institute. Cegelski’s PhD in Chemistry and
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scientists. Candidates with experience in prototyping, optical instrumentation, image processing, or translational device development are particularly encouraged to apply. Required Qualifications: PhD in
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collaborative culture. The Division of Pain Medicine is at the forefront of innovation in pain research, education, and patient care. Our postdoctoral program has successfully transitioned fellows
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would include: Co-developing a hybrid machine learning/process-based model of anaerobic digestion processes Performing techno-economic and lifecycle analysis of microgrids build around novel biogas-fueled
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clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating