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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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upon future funding. Qualifications Required Education and Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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Education and/or Work Experience M.D. or PhD Required Qualifications Ph.D. in any of the following areas: Neuroscience, Pharmacology, Physiology, or Biochemistry. Certificates/Credentials/Licenses Ph.D. in
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. As a Carnegie Doctoral/R2 institution, our world-class scholars instruct about 26,000 students in associate's, bachelor's, master's and doctoral level degree programs. Whether you are seeking the charm
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) and spatial Machine Learning (ML) models Salary and full employee benefits are offered in accordance with Princeton University guidelines. The Term of appointment is based on rank. Positions
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that the candidate has prior research experience in one or more of the following research topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning