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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
on MMS data. Required Knowledge, Skills & Abilities: Computer analysis of digital data from satellite data centers. Physics background analyzing the data. Other Requirements: local residency. Preferred
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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control under high inverter-based resources (IBRs). • Develop and apply artificial intelligence (AI)/machine learning (ML) techniques for power system planning, operation, control, and cybersecurity
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to optimization problems with possible topics covering: Variational quantum algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical
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to public safety, El Paso is the fifth safest large city in the United States. In 2025, U.S. News and World Report named El Paso as the second best big city in the United States in which to live. El Paso is
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation