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Field
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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for this position: PhD in Mechanical Engineering, Structural Engineering, Civil Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation
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Requirements: PhD degree in Artificial Intelligence, Computer Science, or a related field from a prestigious institution. Good written and oral communication skills. Proficiency in developing deep learning
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Strong analytical skills and experience in developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 1 month 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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, we expect machine learning to be employed to improve accuracy and efficiency of numerical methods, combining advanced technology with scientific research. About the Department of Mathematics at UiB
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storage materials, and employing machine learning and high throughput for the discovery of new electrode materials and electrolyte systems. 1. Holds a PhD degree in chemical engineering, chemistry
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Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation of multi-physics systems, especially fluidic problems Expertise in generative