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to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data
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(e.g., transportation networks, manufacturing systems, and truck routing). Assessing the relevance of the intake fraction (i.e., exposure efficiency) of major emission sources as a critical metric for
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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collegiality is at the heart of what we do. The Department’s research structure consists of five Research Groups: Algorithms and Complexity (ACiD) Artificial Intelligence and Human Systems (AIHS) Network
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complex behaviours, including learning, in the small invertebrate model organism C. elegans. We have recently discovered a range of novel dopamine receptors in C. elegans and found that different receptors
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understanding of biochemistry and materials chemistry with the ability to perform assay development. The successful candidate must be able to develop independent protocols for complex novel assays for multiple
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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limited understanding of how tumors systemically impair immune responses. Meanwhile, traditional animal models often fail to capture the complexity of human immune-cancer interactions. This position