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Field
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group has expertise in radiology, biostatistics and machine learning. We have a strong collaboration with research groups at KTH, at KI, and internationally within two EU projects and a new collaboration
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Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
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, develop methods to speed up this development by focusing on machine learning techniques to address sustainability challenges in chemistry research. We will explore novel pathways to process design and
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machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
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Join us in advancing the frontier of plant sciences and making a tangible impact on agricultural resilience through the power of machine learning! We are seeking a highly motivated PhD candidate
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Phenotyping” offers a position from 01.03.2024 as PhD student (m/f/x) for development and implementation of machine learning applications and data analysis in Translational Psychiatry Kennziffer: 8740. Your
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39-21272 Date of filling: As soon as possible Application deadline: 31.12.9999 The Neural Data Science Lab (Prof. Ecker) and the Machine Learning Lab (Prof. Sinz) of the Institute of Computer Science
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validation. Job Requirements: A PhD Degree in AI/Computer Science, Aerospace Engineering, Mechanical Engineering, etc. Strong background in deep learning, machine learning, and/or image processing. Relevant
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. The successful candidate is required to: - Contribute to programming, modelling, and human robot interaction - Contribute to computer vision and machine learning algorithms. - Contribute to the implementation