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, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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, Computational Neuroscience, Computational Psychology or Behavioural Science; Transport Modelling, Transportation Science or Urban Mobility; Data Science, Artificial Intelligence, Machine Learning
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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be designed and deployed efficiently
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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the implications of generative and agentic AI for areas such as leadership, everyday work practices, team communication, professional learning, careers, and inclusion. Doctoral candidates become part of
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analysis, causal inference with machine learning, and deep learning for various health-related domains. The Global Pathogen Analysis Platform (GPAP) is a new international initiative to strengthen global
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/augmented/extended (VR/AR/XR) environments to support learning of scientific concepts and practices at the university-level. The main aim of this work package is to investigate how such cutting-edge