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. - Strong expertise in multiscale/multiphysics modeling relevant to catalysis, and experience with machine learning models. - Deep understanding of reaction kinetics, thermodynamics, and structure-reactivity
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developing models for estimating neuromodulator concentrations. The successful candidate will also help with implementing machine learning approaches to other problems arising in the lab. Required
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these models. A positive attitude and a willingness to face uncomfortable situations with the goal of learning are crucial to success in this position. The PI will work directly with the candidate and seek
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that transforms current AI for Science paradigms focusing on multidisciplinary applications in biomolecular modeling and design, leading to a step-change in Scientific Machine Learning (SciML). They will be
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. The candidate will apply a suite of statistical and physical models for integrating observations of different accuracies, improving predictions of future hazards. The work will further include research, writing
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orchestration technologies such as Apache Airflow or comparable tools. • Experience working with infrastructure-as-code technologies such as Terraform. • Experience supporting machine learning, predictive
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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Methods: Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning
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technologies: ForceDecks, NordBord, ForceFrame - Data visualization tools: Tableau, Power BI - Data analysis & coding tools: R, Python - Machine Learning: Predictive modeling, feature engineering, model
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-Demonstrated experience with AI, machine learning and/or large language model (LLM) applications -Coursework or experience using data in an academic research setting Overtime Status Exempt: Not eligible