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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 5 hours ago
. Experience with LPBF and binder jet machines is crucial for this role. PhD in Mechanical Engineering, Materials Science, or a related field Previous experience in material/process development of metal/ceramics
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biological signals. The project will focus mainly on developing innovative models for biomedical signals with irregular cyclicity and exploring potential machine learning applications. Position Objective
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-time systems (#54), AI, machine learning & data mining (#65), software engineering (#71), mobile computing (#18), and computer security (#88). In the last few years, students and faculty in
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the areas of Machine Learning (ML) and Artificial Intelligence (AI). Focus will be placed on applications of these technologies in cybersecurity and anomaly detection in encrypted network data. The successful
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engineering, mechatronics etc. The PhD degree must be awarded no more than three years prior to the application deadline. Required skillset Analytical understanding of Reinforcement Learning, Dynamics and
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profile: A PhD in AI, preferably at the interface of information retrieval and machine learning; Research background in generative information retrieval, with publications in the leading venues relevant
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-$75,564/year Type of Position Temporary Position Time Status Full-Time Required Education PhD or terminal degree in a related field. Required Related Experience Experience performing experiments, analyzing
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches