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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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are seeking a motivated PhD student to join our team working on realizing learning in novel physical materials, as part of a joint theoretical/experimental research project between AMOLF and the University
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at the University of Amsterdam is inviting applications for a PhD position in mathematical machine learning. The position is part of the research project “Mathematical Foundations for Explainable AI”, funded by
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team of experienced researchers in imaging, machine learning, oncology, and pathology. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when
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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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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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Researcher (R1) Country Netherlands Application Deadline 26 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework
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, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop these machine learning-based proxies together with a
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? Are you interested in coming to the Netherlands for a PhD within a joint doctoral network? If so, we invite you to apply for this PhD position. We are seeking a PhD candidate for the joint doctoral