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organized into 10 work packages (WPs): Six WPs focus on disciplinary perspectives in psychology, education, computer sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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Learning & Technology” (PLT) section of the Faculty of Behavioural, Management & Social Sciences. The PLT section specializes in research on professional learning in and across disciplines and organisations
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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multimodal data integration across organismal domains and data modalities, making use of state-of-the-art methodologies such as systems/network analysis, artificial intelligence and machine learning and/or
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features of military and dual-use space systems Behavioural competencies Education A master’s degree in engineering or a scientific discipline is required for this post. Additional requirements You should
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering
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and scientific activities to building (international) community efforts around data science and machine learning. As Science Operations System Engineer for Cloud Platform development, you will be tasked
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint