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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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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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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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. 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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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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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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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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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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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