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Expert in advanced machine learning such as multi-agent generative AI, LLMs, Diffusion models, and traditional machine learning techniques Expert in CALPHAD-based ICME techniques Expert in combining
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and Performance of Research Experiments (75% of Time Spent) Mechanism based discovery of cancer therapeutics. Characterization of metabolic processes of leukemia stem cells. Use of animal model systems
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well as high-throughput screening strategies to identify small molecular compounds that might serve as novel therapeutic agents in disease using cell culture, kidney organoid, and mouse models. Successful
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and conducting experiments using various mouse models of disease. This position involves investigating how bacterial agents modulate immune responses to develop novel therapeutic strategies, with a
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. Construct machine-learning models for feature-based molecular property prediction and drive the inverse design of ligands with engineered properties. Develop machine-learned interatomic potentials trained
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context of defining novel therapeutic agents in Hematological Research. Key Responsibilities: This position is expected to work independently under the guidance of the principal investigator. Projects will
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photo-bases. The work will focus on modeling of adiabatic and nonadiabatic photochemical processes to capture excited states dynamics using an array of ab initio molecular dynamics methods for excited
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Boston Campus. The Clostridia are a diverse group of anaerobic bacteria that range from agents of human disease to industrial microbes used for renewable production of biofuels and biochemicals. Despite
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such as, but not limited to, agent-based models Experience with Remote Sensing (optical and lidar), Geographic Information Systems (GIS), Python, R, and/or other programming languages or other image
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dynamics enabling naturalistic animal behavior? Our labs aim at building mechanistic models of brain function grounded in a combination of theoretical approaches, neural network-based simulations, and