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the High Performance Computing and Data Center, to be completed on campus by August 2026. This position collaborates with others in the growing machine learning and exoplanet subgroups within the Physics and
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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projects as well as general research involving the application of methods from theoretical physics, mathematics, and machine learning with the goal to understand the brain function. Postdoctoral Fellowships
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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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thermodynamics Design and implement machine learning models for data collection, reduction, analysis, and visualization. Work creatively, independently, and productively. Work as a member of a multidisciplinary
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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
both the NCRC in Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded