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Field
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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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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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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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multiple sclerosis—leads to impaired movement, unstable gait, and reduced quality of life. This project will develop an adaptive stimulation interface that uses real-time position-sensor data to estimate
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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
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PhD Position on Requirements Engineering and Spec-Driven Development Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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Navigators. The academic supervision team includes Dr. Engin Topan and Dr. Rob Benthuis. MULTIPLIER integrates operational data, optimization, and hybrid AI into a decision-support environment that advises
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fabrication of MEMS devices. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before 12 January 2026, and include: A Curriculum