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or a similar area. Demonstrable experience in data analyses linking (pavement) material properties to field performance and programming proficiency. English proficiency as specified at https
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Erasmus MC (University Medical Center Rotterdam); Published 3 Nov ’25 | Netherlands | about 2 months ago
The Department of Medical Informatics at Erasmus MC invites applications for a PhD position at the forefront of health AI and data science. Join our innovative, multidisciplinary team engaged in
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to advance predictive, data-driven production and materials planning incorporating the high variability in high-mix, low-volume (HMLV) manufacturing environments. This position is part of an Industrial
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, exposing the limitations of current detection timelines. This reactive posture is worsened by a visibility gap in the DNS ecosystem. A lack of transparency in registration data, coupled with the short-lived
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. This project will develop models that can extract information from legacy data, and leverage this information in the design of new materials, especially for steel types that can be produced in a more sustainable
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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support systems, specifically simulation games and geospatial digital twins. These systems use detailed representations of a real-world environment and its processes by linking to a myriad of data and
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applying data science tools that integrate both customer-related data and Environmental, Social, and Governance (ESG) information within portfolio management settings. The ultimate goal of the PhD candidate
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data (i.e., large-scale assessments from the LKK project ) (Study 1 & 2); qualitative analysis of child data collected with the mosaic method (Study 3); a mixed-method analysis of the caregiver-child
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hybrid (symbolic and sub-symbolic) AI approaches to help users formulate and translate natural language questions into structured representations that can be linked to geospatial data sources and workflows