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Field
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, machine learning or AI to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics
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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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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 months ago
Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills, Training/Education
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are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is expected to be based
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a
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the postdoc to develop and/or apply new and emerging genetic tools to tephritids. Learning Objectives: In this opportunity, the participant will learn and receive training in functional genomics and gain
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at the intersection of mitochondrial biology, functional genomics, and machine learning. This interdisciplinary initiative focuses on discovering, decoding and engineering mitochondrial microproteins (mito-MPs) with
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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and