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. Fluency in English (C1 level or higher). Hands-on experience with Python and AI/ML frameworks (e.g. PyTorch, TensorFlow, HuggingFace). Solid foundation in machine learning, deep learning, and neural network
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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embrace collaboration and create innovative partnerships to foster sustainability and enrich our university, our culture, and region. Please use the following link to learn more about YSU: https://ysu.edu
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includes both conceptual development and empirical validation. We are looking for a candidate with a background in machine learning or data science and strong software engineering skills. The Ph.D. position
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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. Develop AI and machine learning models for recycling process prediction and decision support, such as forecasting metal recovery, impurity levels, energy use, and emissions. Develop optimization and control
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California Baptist University invites applications for adjunct faculty appointments to teach various Electrical and Computer Engineering Department courses. Review of applications will begin immediately and
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3 Apr 2026 Job Information Organisation/Company C.N.R. - IEIIT Research Field Engineering » Computer engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage
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marketing strategy, supply chain and logistics, digital distribution, and operations research. Leveraging the latest technologies, such as artificial intelligence, machine learning, and large language models
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning