97 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at Princeton University in United States
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leadership by encouraging others to learn and develop, giving clear and direct guidance and feedback on their performance. Encourage and support staff, making sure they are motivated to achieve results. Other
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supply tank etc. Communicate and coordinate with the supervisor. Maintains inventory and records of chemical usage. 5%- Inspection: Inspection: Inspects operating machines and equipment for conformance
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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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computer systems and hardware solutions for the Office of Advancement. Responsibilities Team Lead Responsibilities: Oversee the day-to-day operations (including the prioritization and delegation of tasks and
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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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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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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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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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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