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Field
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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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, and to interact regularly with Dr. Jonathan Campbell to design and execute experimental studies involving animal and cell-based models of metabolic disease. In addition, will also perform the following
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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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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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
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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