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Field
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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, quantum circuits, quantum algorithms (3) Quantum many body computations, including sign problem theory, Quantum Monte Carlo method, DMRG, and machine learning Appointments of postdoctoral fellowship
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engage in industry-university partnerships at the cutting edge of engineering, computer and data science, technology, natural resources management, food systems and climate smart innovation. As a new
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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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Sciences at Texas A&M University Texas A&M Forest Service Texas A&M Veterinary Medical Diagnostic Laboratory As the nation’s largest most comprehensive agriculture program, Texas A&M AgriLife brings together
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
. * Developing computational and algorithmic approaches to understanding the neurobiological mechanism of neurodisorders. * Interact cross-functionally: work with people across the team to find creative solutions
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
. * Developing computational and algorithmic approaches to understanding the neurobiological mechanism of neurodisorders. * Interact cross-functionally: work with people across the team to find creative solutions
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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computational tools, pipelines, or algorithms to improve the accuracy and speed of genomic workflows, particularly for rare variants and noncoding regions. • Functional Follow-up: Implementing functional assays