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genomics data, writing Python code for data analysis, and a downstream R pipeline for post-processing data using standard Bioinformatics libraries from Bioconductor. There will be opportunities
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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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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 languages is
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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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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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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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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
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forecasting. Proficiency with statistical computer languages such as Python or R. Proficiency with relational database systems (SQL) and object-based data stores. Ability to define and solve logical problems
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knowledge of at least one major cloud platform (AWS, GCP, or Azure) Strong programming skills in Python and infrastructure-as-code tools Proficient with containerization (Docker) and orchestration (Kubernetes