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Field
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developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in INGA furthermore
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competencies Education You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science
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-based and machine-learning approaches), the digital twin will provide decision-makers and industry stakeholders with actionable insights about when, where, and how corrosion risk evolves. As a postdoc
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, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion/transformers), multimodal representation learning, and experience in
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(DSO) to resolve local congestion issues. The design of such an edge decision-making platform requires an understanding of distribution systems operation as well as machine learning and optimization
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(completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion
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Sciences at the University of Amsterdam (UvA). BDA works on the development of methodology for data mining, machine learning/deep learning, data fusion, and modelling and application of these methods
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methods, integrating qualitative approaches (e.g., interviews, walk-alongs, focus groups) with quantitative techniques (e.g., computer vision, physiological sensing, environmental monitoring, crowd
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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 2 months ago
Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided
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) with quantitative techniques (e.g., computer vision, physiological sensing, environmental monitoring, crowd behaviour analysis), as well as researching existing sources of knowledge in the literature and