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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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at the intersection of AI, RF, and wireless communication. Your main tasks include developing machine-learning methods for wireless interference detection, mitigation, edge intelligence, and applying AI to optimize RF
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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computing environments. Interest or experience in machine learning, inverse problems, or AI for scientific data. Strong record of research productivity and ability to work effectively in a collaborative
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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and data structures for AI kernels; Scalable systems for machine learning (training, inference, edge); HW-SW co-design for computer vision on novel architectures; Desired skills Advanced knowledge in
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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
machine learning methods like machine learning interatomic potentials is a preferred qualification. This preferred start date is January 2026 and can be negotiated. The initial appointment is for one year
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findings to military stakeholders and the implementation of evidence-based practices within military medical populations. Therefore, applicants should have experience working with, or significant interest in
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visualizations, manuscripts, and conference presentations for dissemination of project results. Required Qualifications PhD in plant biology, genetics, genomics, bioinformatics, computational biology, or related