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Field
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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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, or mental health provision are expected to be higher or lower. The agent based modelling will form an innovation where we attempt to create business models and descriptions of best practices for social
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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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. The models will be based on eco-evolutionary theory and informed with ecological and evolutionary data from public databases and published papers. This combined theoretical and data driven approach includes
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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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to keep the data up-to data with very little effort. Estimate energy and land needs for the realization of the CCU potential. Develop a multi-objective optimization model for individual CCUS projects