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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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expertise in Large Language Models (LLMs), Agentic Systems, as well as strong interdisciplinary teamwork skills and communication skills. About the Stanford NLP Group: Stanford NLP Group focuses on basic
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stress contagion and collective motion, as well as survey data of emotional states and personality metrics. Modeling efforts will focus on agent-based approaches, which may include both simulation studies
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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based
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modeling and simulation tools DESIRED QUALIFICATIONS: Experience using NONMEM, R, Monolix and/or other platforms for analysis of clinical and/or preclinical pharmacology data; development of web-based tools
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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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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 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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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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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