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Field
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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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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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] Subject Areas: Physics / Hard Condensed Matter Theory , Machine Learning , Material Science , Physics , Quantum Information Science , Soft Condensed Matter Theory , theoretical condensed matter physics
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/or interest in applying Social Network Metrics to language and cognitive outcomes. -Have experience and/or interest to acquire expertise in machine learning applied to language modeling. Special
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statistics or machine learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL DEMANDS: Standing, sitting, walking, talking and hearing. No special vision requirements. Lifting up
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preferred. Experience working with NCBI dbGaP, TCGA, COSMIC data preferred. Training in statistics or machine learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL DEMANDS
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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software skills in C++, python. Understanding of computing software development in the HEP environment, familiarity with machine learning (ML) techniques and experience with using ML software packages (e.g
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calculations Materials modeling/electronic structure calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed
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(PhD, MD, or equivalent) conferred by the start date. Proven research and/or professional experience in machine learning and/or natural language processing, with a preference for prior experience working