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About the Opportunity Conduct research on machine learning, control theory, and synthetic biology. The work will combine tools from dynamical systems, control theory, and the theory of algorithms
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thermal signals with high sensitivity and nanoscale spatial resolution, opening access to biophysical structure and dynamics that are difficult or impossible to reach with conventional techniques
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engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based surrogate model discovery are strongly
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, cohesive zone modeling, and will interface with the molecular dynamics group to create customized scripts for implementing. Responsibilties: Conducting experimental research Conducting theoretical research
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at least one of the following areas: excited-state simulation methods, multiscale simulations (QM/MM a plus), catalysis (biological, homogeneous, or heterogeneous), molecular dynamics simulations, sampling
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in probabilistic temporal event dynamics. Required Qualifications: - PhD in computer science, engineering, biomedical data science, informatics with advantage for experience in conducting research
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precipitated withdrawal distinction has clinical significance, and this project aims to detect this separation through model architecture in probabilistic temporal event dynamics. Required Qualifications: - PhD
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dynamics simulations, sampling techniques, machine learning, or quantum computing; experience in two or more areas is a strong plus Strong oral and written communication skills with a willingness to
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dynamics simulations, sampling techniques, machine learning, or quantum computing; experience in two or more areas is a strong plus Strong oral and written communication skills with a willingness to
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a Postdoctoral Research Associate to train in interdisciplinary projects involving developing new AI-driven Molecular Dynamics (MD) simulation methods and apply them to drug discovery in multiple