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coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across discipline
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on the use of Machine Learning algorithms for rapid damage assessment. Research topics could focus on: the definition and use of novel damage sensitive features, physics informed machine learning, transfer
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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understanding and practical experience with machine learning approaches for biomarker discovery and predictive modeling, specifically with hands-on experience in developing and applying neuronal networks
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, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning
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publish in top-tier Computer Vision and Machine Learning conferences? Do you have extensive experience working with industry on applied projects? Are you a passionate leader with a people-oriented approach
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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of biosystems and for extracting knowledge from (vast) sets of biotech data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and
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research Teach courses at bachelor and master level in relevant fields such as artificial intelligence, machine learning, neural networks, computer vision or image analysis and coordinate the teaching