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or acquire further expertise in quantitative intravital microscopy. 2) Computational or Cell biologists (with expertise in quantitative microscopy, statistical modeling, cell culture, and/or biochemistry
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, operations research, computer science, mathematical finance, or a related field, the successful candidate will demonstrate the ability to develop independent research ideas and contribute to advancing our
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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in programming (Python, Julia) (provide evidence with specific examples). Experience with statistical modelling and experimental design. Ability to work in a multidisciplinary team. Strong written and
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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the research program operates in compliance with regulatory agency guidelines. Projects are both independent and in collaborations with faculty members and multilevel trainees within the Department of Pediatrics
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Description Position: Postdoctoral Fellow (no. of vacancies: 1) Start date: Flexible (two-year appointment) Requirements 1) Hold or expect to hold a Ph.D. degree in Statistics, Computer Science, Mathematics
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perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
: Doctorate in Oceanography, Meteorology, Statistics, or similar field. Required Knowledge, Skills & Abilities: Fluent in one or more programming languages (python, Julia, or possibly R) Able to perform
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance