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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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for studying spatiotemporal protein interactions. The postdoctoral fellow will have the opportunity to: - Learn novel research techniques for genome-wide screens - Explore molecular mechanisms of mitochondrial
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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Appointment Term: Initially 1 year, renewable Appointment Start Date: Fall 2025 but flexible Group or Departmental Website: https://www.liwanglab.org/ (link is external) How to Submit Application Materials
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brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
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. Proficiency in programming languages for data analysis (e.g., Python, R) and experience with machine learning, statistical modeling, and wearable sensor data analysis is desirable. We expect you to be able
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning. Proficiency in Python, MATLAB, or R. Strong
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for all UNLV postdocs; and provide professional development programs and networking events for postdocs. UNLV currently employs postdoctoral scholars across a wide range of disciplines. Learn more about