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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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collaboration with theorists, machine learning experts, and other experimentalists. Essential Duties and Responsibilities: Single crystal growth of quantum materials Performing transport and electron microscopy
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore
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the pre- and post-processing of Magnetic Resonance (MR) images (or utilization of existing brain imaging markers), taking peripheral or central biomarkers, and regressing them against those images using
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for evaluation by the closing date of this announcement. Strong experience in using MRI to study brain structure. Extensive experience with machine learning in image analysis, particularly with MRI of the brain
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programming; experience in Python for machine learning and/or digital image processing is preferred. Strong communication skills, both in formal written reports/manuscripts and oral presentations. Language
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States of America [map ] Appl Deadline: (posted 2025/01/10, listed until 2025/07/10) Position Description: Position Description Multiphysics, Machine Learning, and Uncertainty Quantification Postdoctoral Positions Los
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how