98 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Rutgers University
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at the intersection of theoretical chemistry and machine learning. Candidates with experience in method development and high-performance computing are especially encouraged to apply. A Ph.D. in chemistry, physics
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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biology, preferred. Background in immunology or cancer biology research preferred. Experience with mouse colony management and mouse experiments in vivo is highly preferred. Computer literacy with
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Posting Open Date Posting Close Date Qualifications Minimum Education and Experience The candidate should hold a PhD degree in Computer Science, Information Systems, Computer Engineering, or a related field
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capable of viewing gauges, computer monitors, charts, forms, text and numbers for prolonged periods. WORK ENVIRONMENT: Must be available to work flexible hours, including overtime on short notice and
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-making. Among the key duties of this position are the following: Develop and refine computational models (e.g., mechanistic, machine learning, or hybrid approaches) for ICU patients. Integrate and analyze
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limited to artificial intelligence, computing theory (algorithms, complexity), data science, statistics, discrete mathematics (graph theory, combinatorics), game theory, machine learning, optimization
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research laboratory, excellent communication skills, excellent computer literacy. This research over a large span of life science topics and technical approaches, including genetics, biochemistry, cell
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Experience PhD is required along with knowledge of computer programming, statistical analysis, and genome evolution. Certifications/Licenses Required Knowledge, Skills, and Abilities The position requires
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milestone 250th anniversary in 2016, the university is the eighth oldest higher education institution in the United States. More than 67,000 students and 22,000 faculty and staff learn, work, and serve