97 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Princeton University
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(i.e. machine learning, neural nets, LLMs). Prepare results and design figures for reports and presentations. Manage and manipulate data using requested languages, including Python, R, MATLAB, and STATA
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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and maintaining experimental and training equipment. Along with experimentation, the candidate will assist other members in the planning and implementation of surgical procedures, learning relevant
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, Panini grills, slicers, choppers mixers, blenders, frozen dessert machines various hot and cold beverage dispensers, coffee brewers, espresso/cappuccino machines steamer/oven, card reader, and various hand
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. Qualifications High School diploma or equivalent. Candidate must be Customer Service oriented. Must possess physical security experience, past law enforcement, or a military background deemed equivalent to meet
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to thirty pounds of force frequently or constantly lift, carry, push, pull or otherwise move objects. Must operate a variety of machines and equipment including, but not limited to automobile, office
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development and application of novel algorithms and machine learning/AI techniques for extracting insights from biological data sets (genomics, proteomics, imaging, neuroscience), and related areas
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course. The appointment period will be for either the Fall 2026 or Spring 2027 semester. The Anschutz Fellow(s) will teach one interdisciplinary, three credit-hour, undergraduate seminar course for the
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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. Flexibility and dependability. Excellent computer skills, ability to learn the UHS-specific program, Medicat. Be knowledgeable in principles and methodologies (e.g., Plan-Do-Study-Act, Root Cause Analysis