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Associate will contribute to ongoing projects and have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding
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approach to develop chemical probes, investigate biological mechanisms, and evaluate in vivo efficacy. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR
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. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR) as models. PXR and CAR transcriptionally regulate cytochrome P450 3A4 (CYP3A4) and CYP3A5-drug
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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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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medical care, little traffic, and a reasonable cost of living make the greater Bangor area a wonderful place to live. Visit the Maine Office of Tourism to learn more about what the Bangor region has
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that the candidate has prior research experience in one or more of the following research topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning
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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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decision-making for complex infrastructure systems. This position offers an opportunity to contribute to interdisciplinary research at the intersection of civil engineering, machine learning, and systems