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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
discretizations and/or machine learning methods. The ideal candidate should have a strong background in numerical analysis, scientific computing, and/or scientific machine learning. We are particularly interested
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qualifications include: Experience with radio interferometric observing, data processing, and imaging. Experience with modern machine learning / deep learning techniques and software packages. Experience with time
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-centred AI applications. This position is highly interdisciplinary as it supports the advancement of data visualisation, machine learning, and artificial intelligence domains. The Post-Doctoral Researcher
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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to support research groups associated with Institute faculty, in areas such as: ● Machine Learning and Computer Vision ● Natural Language Processing and Data Science ● Biomedical Informatics and
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to science. This is the first large-scale study of its kind, and your results will establish a legacy of scientists working with funding councils to defend their research. Cutting-edge machine learning
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industrial partner, you will design and implement innovative architectures for real-time detection and control of laser processes. This interdisciplinary role combines artificial intelligence and machine
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic