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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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to emerging or re-emerging diseases. Proficiency in programming languages such as Python, R, SQL, or similar tools for data analysis and model development. Experience in working with large and complex datasets
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. Proficiency in Python for data analysis and model development (NumPy, pandas, scikit-learn). Working knowledge of SQL and relational databases. Familiarity with at least one ML or deep learning framework (e.g
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human expert ratings. ESSENTIAL QUALIFICATIONS Graduate student or professional in Computer Science, Data Science, Computational Linguistics, or related field Strong Python programming skills (pandas
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architectures (e.g., convolutional neural networks or transformers); deep specialization is not required. Exposure to Generative AI concepts and large language models (LLMs) is a plus. Proficiency in Python
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in bioinformatics or genomic data analysis, and possess strong programming skills in languages commonly used in bioinformatics, such as Python and R. Candidates should also have experience with genomic
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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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-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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. 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