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Field
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning
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road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and supporting
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to conduct research, solve complex technical problems, and develop innovative solutions. Experience with parallel systems, machine learning/AI, or performance benchmarking is highly desirable. Ability to work
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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an industry partner. Experience with research software, data pipelines, and simulations, machine learning, high-performance computing, CANFAR, or advanced data systems. Evidence of mentoring or supervising
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian