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Field
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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected
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» Informatics Computer science » Modelling tools Mathematics » Applied mathematics Mathematics » Algorithms Mathematics » Computational mathematics Mathematics » Statistics Physics » Statistical physics Physics
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applications in the media change journalism, the public sphere, civic engagement and economic competition. We will develop and test new AI-applications to help solve problems such as disinformation, algorithmic
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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
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sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit, and excellent collaboration skills. You will be responsible for the collection
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with
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, reconstruction algorithms, and data acquisition techniques to ensure high-quality inputs. Collaborate with clinicians: Work with medical specialists to validate the clinical utility of your algorithms and ensure
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational