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staff with a safe and secure environment. Through our partnerships across the university and local, state, and federal law enforcement agencies, we use a blended service model to meet the needs of our
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
to accelerate technological maturity (TRL), validation, and the transfer of scientific results. Main Responsibilities: Application of advanced Machine Learning, Deep Learning, reduced-order models, and physics
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About Us For more than 60 years, Kennesaw State University has been known for its entrepreneurial spirit and sense of community. A leader in innovative teaching and learning, Kennesaw State is
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, organization, cleaning, and management of speech and language datasets. Support the development, testing, and refinement of machine learning and artificial intelligence models. Conduct literature reviews related
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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machine learning algorithms to support the traceability of the beef’s geographical origin. She/he will participate in all stages of the project, including planning and supervision of sample collection and
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and AI, including experience in machine learning software development. E3. Experience in training deep learning models relevant in research projects. E4. Experience of applying good software engineering
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uncertainty, since misclassifications can lead to poor energy management decisions or inappropriate building automation responses. The goal of this project is to design and validate a machine learning framework
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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modern machine learning, with broad implications for both theory and applications. The PhD project focuses on the theoretical foundations and algorithmic design of discrete gener- ative models, situated