63 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" research jobs at Pennsylvania State University
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opportunity in meteorology and atmospheric science and planetary atmospheres. The ideal candidate will have expertise in synoptic scale meteorology, scientific computer programming (python and/or MATLAB), data
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, translational assays, and molecular techniques, and offers the opportunity to collaborate with and learn from leading experts in leukemia and sphingolipid field. Key Responsibilities: Test therapeutic efficacy in
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- Undergraduate student in Education, Business, Communications, Media Studies, Sociology, or a related field. Strong writing, organization, and communication skills. Familiarity with or willingness to learn basic
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procedures. Basic computer skills, including use of shared database and spreadsheet platforms, will be required. Sound organizational skills and a professional working attitude are essential. BACKGROUND CHECKS
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, and English language arts teaching and learning Prior research experience in any of the following: Adobe Premiere Pro; Excel; web design and development; online engagement; video coding and analysis
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, Aerospace, or related fields). Strong attention to detail and ability to follow experimental protocols. Eagerness to learn and contribute to ongoing research projects. Excellent teamwork and communication
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computational biology and bioinformatics, with experience in: Applying deep-learning or AI-based methods for image denoising. analysis of whole-genome sequencing (WGS) or other large-scale sequencing data
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ambitious to learn. The qualified candidate needs to be detail oriented and follow SOPs and experimental plans. Job duties to include: Collecting, documenting, and distributing brain samples to support
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or desire to learn qualitative research methods (e.g., coding, thematic analysis). Apply through Penn State Careers- Careers | Human Resources , with a one- to-two-page resume and a short (no more than one
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insects used in experiments Collect and curate data Assist in the development of smart traps using Raspberry Pi systems Learn data analysis techniques in R and conduct analyses Share results of research