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Field
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models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. Research areas include Representation Learning, Machine learning and Optimization
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 21 hours ago
Duties and Responsibilities Responsibilities: 1. Conduct research on AI-driven methods, including reinforcement learning and diffusion models, for molecular and antibody design. 2. Develop algorithms, run
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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: OBJECTIVES | FUNCTIONS Develop algorithms for pre-processing of spectra, by analysing user-uploaded files to identify their origin, confirm spectra presence, and assess spectrum quality. Implement novel AI
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and implementation of algorithms for optimal resource utilization. Installation and evaluation of the quantum network and integration of quantum and classical cryptographic systems. Research and
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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validate algorithms for estimating heat stress, counting animals, and estimating mass in real-time. Although research is needed to integrate One Health assessments across the soil-pasture-animal continuum
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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process and the results obtained. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - To contribute to the specification and development of algorithms for optimizing energy systems with