407 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" Fellowship positions in United States
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Field
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are offered an opportunity for an independent research project using lab data to gain experience conducting ecological data analysis, manuscript writing, and publishing in peer-reviewed journals. Learning
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pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities
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diseases such as Japanese encephalitis, Rift Valley fever, and related diseases. Learning Objectives: The fellow will have opportunities to learn field-based techniques related to survey and manage arthropod
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What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
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) in addition to Medicare and Medicaid claims. Our team also has extensive methodologic experience, including natural experiments/econometrics and various machine learning techniques. The Fellow will
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adverse events to inform standard ER analyses for safety and provide complementary tolerability data to improve dosage optimization strategies in oncology clinical trials. Learning Objectives: As an ORISE
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About the Opportunity Khoury College of Computer Sciences is looking for a Distinguished Research Fellow. Responsibilities: The Distinguished Research Fellow will be expected to start-up their own
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documenting progress on data processing. Opportunities may also be available to participate in field data collection at various locations in the Pacific Northwest. Learning Objectives: As an educational
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to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning Objectives: Under guidance of a
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Fusion Tribrid MS and Waters Q-ToF instruments are highly desired. Experience handling and analyzing large-scale MS, MS(MS) and/or proteomics-like datasets using statistical and machine learning techniques