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/C++, FORTRAN and/or Python. Experience working with geo-spatial information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software
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feed into this vision. The intended start date is July–August 2026. Job requirements PhD in machine learning, artificial intelligence, computational chemistry, computational materials science, or a
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, robustness, calibration, bias/fairness, and/or adversarial stress-testing. - Solid programming and ML/NLP engineering skills in Python and ideally modern deep-learning stacks (e.g., PyTorch/JAX, HuggingFace
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controls, using deep learning and explainable AI Communicate and discuss results with stakeholders to integrate the findings into water management practices Interpret, publish and present findings in peer
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Computer Science, on topics related to Artificial Intelligence and Machine Learning, Human-Robot Interaction and 3D Vision/Multimedia. Experience with deep neural models and algorithms for 3D spatial reasoning and
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of faculty at SUNY Polytechnic Institute and the University of South Florida, consisting of mathematicians, physicists, computer scientists, and engineers investigating applications of deep learning
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of Civil Engineering in the College of Engineering at the University of Texas at Arlington invites applications for a Postdoctoral Research Associate. The position focuses on applying AI, machine learning
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and methods for advancing the research effort Design and carry out computer experiments on deep learning and related robotic simulations Collaborate with other engineers to create prototypes of embodied
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to streamflow as a function of climate and landscape controls, using deep learning and explainable AI Communicate and discuss results with stakeholders to integrate the findings into water management