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Field
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, Biostatistics, Computer Science, Statistical Genetics, or a related quantitative field (by the time of appointment). Strong background in statistical or machine learning methodology, optimization, or high
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students to drive forward a diverse research program focused on machine learning and policy evaluation. The following tasks are designed to culminate in co-authorship on research papers: Plan and perform
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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of machinery or tools used to perform work, moving vehicles, electrical current, working on scaffolding and high places, or exposure to chemicals. Atmospheric Conditions: Conditions that affect the respiratory
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datasets, performing statistical analyses and machine learning approaches to identify biomarkers of treatment responses. The candidate will also develop and implement bioinformatics pipelines in a high
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of machinery or tools used to perform work, moving vehicles, electrical current, working on scaffolding and high places, or exposure to chemicals. Atmospheric Conditions: Conditions that affect the respiratory
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equipment, computer resources, and yearly research support. Appointments are for one year with the possibility of renewal pending satisfactory performance and continued funding. Candidates are encouraged
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limited to: Performing neuro-pupillary testing that provides quantitative analyses of the health of the eyes and or nervous system. Performing assessments on paratroopers who have sustained high magnitude