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Field
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investigators in various scientific domains, such as physics, chemistry, and biology, conveying machine learning results and collaborating to explore the insights the models have discovered. - Participated in
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sequencing, proteomics, and metabolomics; interpretation of datasets and clinical data using advanced statistical methods and machine learning algorithms to identify correlations between molecular alterations
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related to battery materials, correlated electron calculations, including via DFT+U, supercells, dynamical mean field theory or experience in defect and/or alloy calculations, machine learning, and other
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discoveries. Who You Are: Ph.D. with a proven track record of excellence in Computer Science and Machine Learning, with substantial domain experience in biology and genomics. Must have advanced at least one key
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 3 months ago
of machine or deep learning models to identify patterns of codon usage in yeast genomes, 2) the implementation of tRNA-sequencing across diverse yeasts and conditions, and 3) the construction of tRNA deletion
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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statistics, physics, computer science, artificial intelligence, or a related quantitative field Experience with mathematical/statistical modeling and/or machine learning/Al methods Strong interest in
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidates will work in close cooperation with our current PhD students within the PhD programme, and