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Field
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scintillations during storm and quiet time. Primary responsibilities include analyzing GNSS measurements and other satellite data and conducting data-model comparisons. In accordance with USCIS regulations
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data-intensive operations in scientific and AI applications. Investigate machine learning techniques to inform heuristic methods for routing optimization, bridging theoretical insights with practical
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL
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to learn more about what the Bangor region has to offer. Qualifications: Required: Doctoral degree in engineering, environmental science, geoscience, or related field. Knowledge and experience in engineering
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essential. Experience in applying machine learning approaches particularly large language models. Ability to work within a multi-disciplinary team environment on scientifically challenging problems
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at the intersection of AI/deep learning, bioinformatics, mathematical modeling, computational topology, scientific machine learning, for systems biology, single-cell genomics and protein design. The candidates will
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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engineering tools, including process modeling, process control, machine learning, and artificial intelligence, to support ongoing proposal efforts in advanced pharmaceutical manufacturing. The successful
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machine learning algorithm to improve the modeled surface meltwater in the Goddard Earth Observing (GEOS) model. Advocating and adapting the model based on collaborator feedback is key to the success