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, you will combine continuum mechanics, machine learning, experimental imaging, and mechanical testing to design and characterize self-sensing soft matter capable of revealing its mechanics from
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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profile: A PhD in AI, preferably at the interface of information retrieval and machine learning; Research background in generative information retrieval, with publications in the leading venues relevant
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from, collaborate with, support, or improve humans; Deep Learning for Perception: Use of deep learning algorithms for computer vision, image and audio processing, and models of perception. The focus is
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generate document identifiers given a user query. This paradigm departs from traditional multi-stage retrieval pipelines and instead integrates the indexing and retrieval process into a single, end-to-end
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science, engineering, physics, mathematics or a similar domain. There is a strong preference for an applicant with a biomedical background. Experience with medical image processing, histopathology, computer vision
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engineering Engineering » Design engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 5 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable
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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 computational modelling approaches
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Specific Requirements We expect you to have A master’s degree (or equivalent) in urban planning/urbanism/architecture, computer science, human-computer interaction, or related field. Interest and experience
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intelligence and machine learning and/or computational modelling approaches. Moreover, the candidate will have a leading role in expanding and professionalizing the growing computational biology research