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conditions. This uncertainty increases the volatility of short-term energy prices, and thus makes it much more challenging to make economically viable energy trading decisions. One way to respond
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, for example, decision-making processes, coordination mechanisms, and the allocation of roles and risks among participating actors. The project consists of addressing the following main research questions: How
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enabling on-demand, local, and potentially circular production of spare parts — but effective decision-making frameworks are still lacking. In this PhD project, you will develop models and methods
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coordination. You also need to align with the work of partners from social science& humanities (SSH), such as on decision making or integration of existing human expertise int the technical systems, and ensure
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Are you passionate about the future of circular manufacturing and intelligent quality control? Join us to develop AI-powered frameworks that improve real-time decision-making in additive
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scenario analyses, you will help build actionable insights and decision-support tools for responsible AI adoption. You will work closely with academic experts and industrial collaborators, contributing
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have helped us understand global market dynamics and policy impacts. Yet, they often rely on assumptions of rational decision-making and overlook the diversity of behavioral drivers—such as institutional
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of PhD and postdoc researchers excited about making theoretical and algorithmic contributions in intelligent decision making. Apart from dealing with the scalability challenge inherent in modern AI
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uncertainty increases the volatility of short-term energy prices, and thus makes it much more challenging to make economically viable energy trading decisions. One way to respond to this challenge is to
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, which assumes infrastructure should serve and afford an ever-increasing demand, loses validity. Infrastructure policies can impact decision-making and infrastructure use by prioritising, shifting and