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: Focus Data science and/or statistical methods development addressing questions of health, technology, housing, education, innovation and others impacting national and international urban communities
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at the intersection of theoretical chemistry and machine learning. Candidates with experience in method development and high-performance computing are especially encouraged to apply. A Ph.D. in chemistry, physics
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. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and
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environmental and pharmacoepidemiological research methods and programming skills for large relational databases. This is a two year position that will be supported by an active NIH funding. The successful
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. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and
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other substance use disorders. Mentorship for developing research ideas, designs, and methods, conducting clinical trials, and writing grants will be provided. Clinical opportunities and supervision
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Significant experience with methods for extraction and analysis of environmental plastics is required including FTIR (ATR- and FTIR-microscopy), Raman spectroscopy, and/or Pyrolysis GC-MS. Experience conducting
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., concept bottlenecks, prototype/contrastive methods, attribution) and evaluating calibration and uncertainty is essential. Certifications/Licenses Required Knowledge, Skills, and Abilities Strong software
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epigenome dynamics in immune responses. By using genetic, genomic and biochemical methods, the individual will identify/analyze/characterize molecular determinants for chromatin looping in plants
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of areas including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational methods