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advanced machine learning feature detection to enable evolving subsea scenes to be robustly aligned across repeat surveys. You will develop probabilistic models to detect subtle changes under noise and
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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
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/ Population-scale multiome immune cell atlas reveals complex disease drivers https://www.medrxiv.org/content/10.1101/2025.11.25.25340489v1 A structure-informed deep learning framework for modeling TCR-peptide
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Location Lexington, KY Grade Level 12 Salary Range $62,400-111,634/year Type of Position Staff Position Time Status Full-Time Required Education BS Click here for more information about equivalencies: https
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Location Lexington, KY Grade Level 12 Salary Range $62,400-111,634/year Type of Position Staff Position Time Status Full-Time Required Education BS Click here for more information about equivalencies: https
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to uncovering the intricate relationships between human activities and the environment. Through innovative tools, methods, and a deep commitment to sustainability and justice, we explore how societies shape—and
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, a deep understanding of their field, and broad scientific interests beyond their area of expertise. Nature Materials (https://www.nature.com/nmat/ ) is a monthly multi-disciplinary journal aimed