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large EHRs, claims, or omics datasets. Ability to conduct research independently and within a team. Minimum Qualifications PhD in data science, biomedical informatics, computational biology, neuroscience
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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. Position Responsibilities Develop and implement machine learning and deep learning models to analyze and interpret high-throughput functional genomics data, such as ChIP-seq, RNA-seq, and ATAC-seq
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computational biology/chemistry, machine-learning for biological or chemical data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve
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data storage capacity to accelerate research in intensive computing and large-scale data analytics, commonly referred to as Big Data. This characteristic distinguishes the HPC center at the university
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computational biology/chemistry, machine-learning for biological or chemical data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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/ Data Analytics Artificial Intelligence, Machine Learning and Autonomy Appl Deadline: 2026/06/17 11:59PM (posted 2025/06/17) Position Description: Apply Position Description The Skinnider Lab
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning