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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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Systems, Quantum AI, Blockchain AI, AI for Autonomous Systems Foundational courses (for Connect pathway students): Python programming, Mathematical Concepts, Research Methods and Scientific Writing
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personal style willing to receive assistance and coaching when needed from the faculty lead. Prior experience in a diverse higher education institution preferred. Knowledge of Python and/or R programming
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personal style willing to receive assistance and coaching when needed from the faculty lead. Prior experience in a diverse higher education institution preferred. Knowledge of Python and/or R programming
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workloads including embedding generation, LLM inference, and cognitive search. Develop Snowpark Python transformations, UDFs, and machine-learning features. Implement vectorized storage, model-serving
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, materials science, chemical engineering, bioengineering, or a closely related field. The candidate should have strong problem-solving skills and be highly motivated. Programming skills (Python or Fortran
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Ph.D. in science (chemistry, physics, biology, etc.) or engineering required 7+ years of experience in computational physics or related area Proven experience coding in C/C++, Fortran, and/or Python
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skills with proficiency in Python, TensorFlow/PyTorch, and experience with containerized deployments and MLOps practices. Data Pipeline Engineering: Extensive experience with end-to-end data pipelines
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ML systems into production environments, with a focus on performance, robustness, and scalability. Domain expertise in NLP, computer vision, or speech processing. Proficient in Python for software and