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Field
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discretionary funds with opportunity to apply for additional internal funds Access to shared research facilities and high-performance computing Protected time and programmatic opportunities for
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chemical, mechanical, completion-based, and operational flow control strategies to optimize geothermal energy production performance. Research will combine reservoir simulation, field data analysis, and
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National Center for Toxicological Research (NCTR) | Jefferson, Arkansas | United States | about 2 months ago
training in AI-enabled regulatory science: Generative AI and Deep Learning: Develop and evaluate GAN-based models using Python and high-performance computing. Computational Toxicology: Curate
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project partners will be used. As a PhD candidate, you are expected to perform the following tasks: You will conduct high-quality research under the supervision of Prof. Jeroen Famaey as a member of the
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bioinformatic analysis of Next Generation Sequencing data, including command-line work in a high-performance computing environment. • Programming or scripting skills (e.g., R, Python, bash). • Excellent
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, and simulations, machine learning, high-performance computing, CANFAR, or advanced data systems. Evidence of mentoring or supervising undergraduate students, graduate students, junior researchers, or
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competencies at LLNL. Fellows participate in experimental, computational, and/or theoretical work, and will have access to LLNL's extensive computing facilities, talent pool, specialized laboratory facilities
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on R&D and innovation, the changing nature of working tasks, and business performance. Professor Fulvio Castellacci and Associate Professor Markus Bugge coordinate the TIK Centre’s work in SURE-AI