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to the timing of events and the results from performance optimization need to be included in the models. This PhD position will address these challenges by developing new timing-aware distributed supervisory
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quality that is not visible in blood or clinical characteristics. By combining the results of AI-driven image analysis of histological samples conducted in this PhD project with biomarker data and outcome
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. The verification of distributed systems, however, is notoriously difficult. This PhD research is envisioned to broadly follow one of three directions, but can be adapted to suit the interests of an excellent
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join us as a PhD candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating
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) are looking for a: PhD candidate, Cryptographic Hardware and Design Automation Project description We are looking for a PhD student to work on Cryptographic Hardware and Design Automation. The project is
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. FEBRI, the graduate school and research institute of the Faculty of Economics and Business has available One PhD position in the field of “Industrial Digital Platforms for Digital Product Passports
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to distributed energy resources, enabling energy sharing, reducing grid congestion, and enhancing sustainability. The PhD project will focus on the governance models adopted for energy hub platforms. These include
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interdisciplinary team as a PhD candidate in the BDUC-iN study! The position Are you passionate about haemostasis and committed to advancing the understanding and care of people with bleeding disorders? Join our
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the increasing demand for more complex components, a step change in model accuracy and associated material characterization methods is required. For this purpose, we are looking for two PhD candidates to tackle
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-like dynamics in synthetic systems remains a major challenge across physics, chemistry, biophysics, and materials science. In this PhD project, you will: Develop an experimental platform to study 3D self