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Field
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Zheng, and other collaborators to develop statistical and machine learning methods for medical decision-making in cancer prevention and early detection. Key areas of focus include, but are not limited
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: Proficient with Python, R, and Linux/Unix systems. Strong skills in computer scripting and pipeline development. Experience in project management Experience in network analyses. Previous post-doctoral
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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to junior researchers, graduate students, and lab technicians, fostering an environment of learning and collaboration. Statement In this position, the Postdoctoral Fellow will frequently encounter complex
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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appointment) in a relevant field, such as geodynamics, seismology, geophysics, or computational science. Ability to sit for extended periods (e.g., 6–8 hours/day). Visual acuity to read computer screens and
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with biological tissue. Duties of the position will include designing optical spectroscopic imaging technology, using numerical simulation and machine learning tools to extract diagnostically-relevant