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independently to develop therapeutic agents against mutant p53 and other oncoproteins using artificial intelligence, monoclonal antibodies, and DNA vaccines. The positions offer a unique team-based science
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particularly the CAND multiscale drug discovery platform developed by the Division of Bioinformatics at the University at Buffalo: Integrating the CANDO drug discovery platform with LLM-based reasoning models
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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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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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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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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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chain and materials flow analysis, and agent-based modeling. The candidate would build on established modeling tools at Argonne in each of the modeling/analysis areas, but there may be opportunities
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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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microbial agents affect beta cell function and modulate the immune system. This research includes working with both human and mouse models to analyze immune responses, microbial influences, and the potential