725 programming-"https:"-"FEMTO-ST"-"UCL"-"https:"-"https:"-"https:" Postdoctoral positions in United States
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orientation, marital status, parental status, gender identity, gender expression, and genetic information in the university's programs and activities. Retaliation is also prohibited by university policy
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Experience A PhD in Physics, Astronomy, Computer Science, or a closely related field is required. Experience with HPC systems, jax, python, or similar programming languages, machine learning, and transient/AGN
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physics, dark matter and neutrino mass measurement. There are also active programs in theoretical gravitational physics, gravitational wave physics and cosmology. Postdoctoral scholars will be members
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access vital statistics and Medicare/Medicaid claims data and will be advised by Dr. Kyle Aune. Candidates for this position should have experience in the areas of epidemiology, statistical programming
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sequencing data, whole-genome alignments, and assemblies is preferred. Candidates should be familiar with computational biology techniques and scientific programming. Prior experience with cloud and/or high
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the human and other genomes. The successful applicant must have the ability to plan, execute, interpret, document, and troubleshoot independent research and to collaborate effectively with other computational
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(contingent on performance and funding availability). The postdoctoral scholar will work in Dr. Jiayi Sun’s group and lead innovative research using new ALMA and JWST data from several large programs
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related inflammatory conditions. Responsibilities Plan, design, and execute experiments of considerable scope and complexity in cardiovascular and endothelial biology. Investigate mechanisms
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to: • Work safely and independently in a laboratory setting • Plan and execute research in collaboration with other researchers • Synthesize and characterize polymer precursors and products
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calculations, sample size determinations and statistical analysis plan. 3. Apply machine learning approaches to integrate diverse datasets collected from study participants to identify subtle differences