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central area of expertise. The successful candidate shall demonstrate deep knowledge of LCA methodology and tools, and show strong competencies in methodological development and application across various
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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the Pytorch library and running deep learning models. The successful candidate will work closely with a team of researchers and faculty members in the ClinicalNLP lab led by Dr. Hua Xu. More information of the
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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Qualifications: Experience with aging populations or neurodegenerative diseases Familiarity with deep learning and advanced statistical approaches to neuroimaging data Prior publications in relevant areas Required
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knowledge of methodologies such as deep and statistical learning. Informal enquiries may be addressed to Prof. Andrea Vedaldi (email:andrea.vedaldi@eng.ox.ac.uk) For more information about working at
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computational pipelines and deep learning of imaging. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications: No additional
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You