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Field
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program of independent research with the primary goal of improving the algorithm codes used to characterize the Earth’s gravitational field. Duties will include specialized scientific/numerical analysis
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Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). Examples on areas of research interest include but are not limited to: AI/machine learning algorithm development
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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new MRI techniques for motion-robust imaging, real-time image processing, and/or deep learning. The work includes MRI pulse sequence design for various MRI techniques and technologies, algorithm and
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. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). A broad understanding of machine learning methodologies and
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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, innovative system designs, and advanced algorithms to shape the future of immersive experiences. QUALIFICATIONS: Ph.D. in Computer Science, Electrical Engineering, or related fields. Strong background in AR/VR
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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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health and disease, and experience in the algorithms used to analyze these datasets. The appointee will ultimately create an independent research effort with dedicated extramural funding that complements