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of manufacturing process parameters. Rather than running expensive physical experiments at random, the goal is to design experiment sequences that maximally reduce uncertainty about which process conditions lead to
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previously acquired knowledge. We will study collaborative learning scenarios, where multiple devices or sensors jointly process and learn from data streams. Such settings introduce additional challenges
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to align with the interests and educational background of the PhD student. Potential areas of focus include materials synthesis and characterisation, the development of new characterisation techniques
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. Additional qualifications Experience with interdisciplinary work, e.g., through studies that include multiple academic fields. Experience with one or more of the methodologies relevant to the project: work
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supervision, including participating in the project and third-cycle course. A detailed description of your role will be developed in discussion with your supervisors, aligning with your research interests and
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and align economic activities with sustainability goals, as mandated by regulatory authorities (e.g., BIS, ECB). Adopting a socio-technical approach, the program focuses on three levels of enquiry