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ecological systems with frequency-dependent selection. Planned projects use dynamical systems, stochastic differential equations and agent-based models, statistical methods for parameter inference, network and
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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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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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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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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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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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. 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