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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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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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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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operates in English, strong English skills are essential. Further project details will be discussed during the interview. The anticipated start date is no later than February 2026. Profile PhD degree in
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 4 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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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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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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Salary: $62,500 annually Special Instructions: A cover letter and resume are strongly recommended. You may upload these in the CV/Resume section. Required Education and Experience Appropriate PhD in a
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the Division of Engineering, New York University Abu Dhabi, is seeking a highly motivated Postdoctoral Associate to advance cutting-edge research in machine learning (ML). Our lab explores the intersection