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Field
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simulation tasks. Develop and maintain HPC-ready software workflows for distributed training, large-scale inference, scalable data ingestion, and data management on leadership-class computing systems and
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on campus full-time, following Yale University’s postdoctoral policies. Qualifications We are seeking candidates who meet the following criteria: A PhD or equivalent degree, already obtained, in a related
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of Position Temporary Position Time Status Full-Time Required Education PhD or equivalent in biology or related field/9 years Required Related Experience Molecular Biology RESEARCH: Cell culture, animal work
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scientists, software engineers, data managers, and collaborators across the Mahidol Oxford Tropical Medicine Research Unit, and the wider Tropical Network Programme. Wet lab work will not be involved in
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at the forefront of emerging technologies and advances in genomics, bioinformatics, and computational biology. Required Minimum Qualifications: PhD in Genomics, Bioinformatics, Molecular Biology, or a related field
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manuscripts based on your own work and collaborating on joint research publications. Ensuring, where appropriate, that software outputs are provided to ensure maximum utility. Presenting papers at conferences
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Biology, Environmental Sciences, Agricultural Engineering, Forestry Engineering, or related disciplines associated with ecology, biodiversity, agronomy, or natural resource management. 2. PhD in Ecology
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The successful candidate will have completed a PhD in chemistry education or related field (ABD will be considered), and will be performing research in line with the PI's research program. The postdoctoral scholar
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contributions include detector development, trigger and DAQ, physics and performance analysis as well as software development and computing. The LunDMX group currently consists of a postdoc, three faculty
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to work well with minimal supervision and with PhD students and external collaborators. Experience related to performance analysis and optimization, GPU architecture, and code generation are highly