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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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one of the following: Econometric methods for causal inference; Data science and machine learning; Survey design and analysis; Qualitative analysis skills specialized in policy and geopolitics A good
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informatics approaches (e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and
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Health, Environmental Health, Biological Sciences, Biostatistics, Data Science, preferably with relevant experience. Prior experience with machine learning is a plus. Recruitment is open immediately and
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to the work described herein. Qualifications Job Requirements: • PhD in Biostatistics, Bioinformatics, Computational Biology, or other related fields. • Strong foundation in statistical modeling, machine
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technology. Key Responsibilities: Collaborate with partners from both the academia and the industry to lead and/or conduct innovative research on, but not limited to transfer learning, explainable machine
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, physics, artificial intelligence, machine learning, topological data analysis, and statistics. We are interested in analyzing big data of complex systems such as DNA, RNA, biological networks, social
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. Coordinate procurement and liaison with vendors/suppliers. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements A PhD degree in computer or