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, patents, and proprietary sources to train and validate predictive models. Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software. Collaborate with synthetic chemists
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pyrochimical processing of oxide, sulfide, or silicate ores. Familiarity with thermodynamic simulation software (e.g., FactSage, Thermo-Calc, etc). Previous involvement in industry-oriented or collaborative
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elements methods Modern machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
/therapeutic response, and advance basic scientific investigation into disease processes. The ideal candidate will have a strong background in both the mathematical and software sides of machine learning, with
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on reliability, security, and resilience of microgrid systems. This includes advanced control system design, modeling and analysis, control architecture design and communication-based control and optimization