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), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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that promote inclusivity, fairness, and productive discourse online. We seek a candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running
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. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing
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predictive framework linking genomic data to extinction risk, working at the interface of evolutionary genomics, simulation modelling, and machine learning. By integrating forward-in-time simulations, real
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resonance spectroscopy, imaging (MRI), Applied Mathematics or Machine learning. We are looking for talented, highly-motivated experimentally skilled young scientists with Master degrees or equivalent or PhD
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description The successful
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research projects in their field of expertise. Assists with the preparation and cleaning of worksite. Analyzes research data and summarizes results. Writes and may contribute to research papers, articles