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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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, comparative genomics, machine learning, and evolutionary analysis to address fundamental questions in molecular biology and human disease. Responsibilities Develop computational pipelines for the discovery
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Families (DCF), the Program for Specialized Treatment Early in Psychosis (STEP) now has built a statewide Learning Health System, called the STEP Learning Collaborative. STEP is the Hub for a network of
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of spinal fluid and other tissue samples Basic statistical analyses If desired, candidate will learn additional techniques in immunology and/ or molecular biology Mentoring: The candidate will be mentored by
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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practices in cataloging and metadata services, and developments in metadata (Linked Data, BIBFRAME, entity management, etc.). 4. Excellent computer skills. Experience with cataloging software such as OCLC
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staff. 4. Well-developed computer skills, database management, and demonstrated ability to adapt to new software programs. 5. Knowledge of the application and review process for a highly selective
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organizational and administrative skills, attention to detail, accuracy, and ability to collaborate. 3. Ability to juggle multiple tasks and meet demanding deadlines. 4. Excellent computer skills (advanced