475 parallel-computing-numerical-methods positions at University of Pittsburgh in United States
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of complex instructional materials and the integration of instructional technologies. Helps lead the work and strategic planning of an instructional, curricula, or training development program within a
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tabular data Utilize statistical methods to analyze data and generate actionable insights Collaborate with multidisciplinary teams to transfer complex algorithms into deployable software solutions Provide
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to ensure ongoing compliance with institutional review board requirements. Recruits research subjects through approved methods and may conduct qualitative research interviews in-person or digitally while
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the position, the required skillset includes: Strong background in electromagnetic and optical modeling using tools including Ansys, Comsol, Matlab, and other methods. Expertise in wet chemical synthesis
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queries for extracting data, preparing static and dynamic reports and finding appropriate methods to visually communicate data insights to diverse audiences. The senior data analyst will also lead the
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two dermatopathology fellows. Our teledermatology program provides synchronous and asynchronous care to patients throughout the state and is utilized for inpatient consults and in-office e-consults with
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, develops, and edits text-based, audiovisual, and multimedia content for online and hybrid programs. Assesses instructional delivery and effectiveness, recommends teaching methods, and implements instruction
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methods. Detailed tracking of lineage, health status, and genotype distribution, with close alignment to experimental demand and protocol approvals. Coordinating with veterinary and compliance staff
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to treat genetic diseases, duties include molecular cloning of DNA constructs, mammalian cell culture, detection and quantification of genetic information changes via sequencing and imaging related methods
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tools to assess the sustainability and environmental impact of a product, process, or activity. Life cycle assessment (LCA) principles, methods, tools, and challenges will be explored throughout