46 machine-learning-and-image-processing-"RMIT-University" positions at AALTO UNIVERSITY
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, the method development will include machine learning based image recognition/processing techniques to enable computational microscopy and experimental matching. For position 3, the method development will
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Aalto University is looking for an Postdoctoral Researcher in Artificial Intelligence / Machine Learning Engineering [Academic Research Software Engineer] to a postdoctoral-level position. The
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: PhD or equivalent degree in Robotics, Computer Science, Machine Learning, AI, Control Engineering, or a related field. Excellent programming skills and experience with related tools and software. A
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Postdoctoral researcher in the field of the development of bio-based processes specializing in process design and ex-ante analysis The postdoctoral researcher will work as expert in process design and modeling
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to you in order to be able to do systems identification and model process dynamics using traditional and advanced tools such as machine learning. Your role and goals You will work in collaboration with
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candidate should be able to teach topics such as: Heat and mass transfer as well as basics of fluid flow Basic thermodynamics and phase equilibria Separation process principles, including but not limited
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Postdoctoral researcher in the field of the Life Cycle Assessment (LCA) of bio-based processes and products The postdoctoral researcher will work as expert in LCA for bio-based processes and products from wood
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chemical reactions and assembling functional nanomaterials from bottom-up in scanning tunnelling microscopy (STM). The project is tightly linked to machine learning algorithms in images (image classifier
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learning or deep learning in image post-processing Experience using optical tools, e.g., high-speed cameras, intensifiers, spectrometers, lasers, etc. The applicant for the position of a doctoral Candidate
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems