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, and the public to join us, through educational programs, artistic collaborations, and museum partnerships. We’re searching for answers to life’s biggest questions, and we need your help. As part of
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PhD position - Modelling the emergence of information transfer in prebiotic self-replicating systems
, and the public to join us, through educational programs, artistic collaborations, and museum partnerships. We’re searching for answers to life’s biggest questions, and we need your help. As part of
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social factors. Specifically, the project will study which factors accelerate pro-environmental behaviour change, particularly focusing on feedback loops. The project connects research and policy-making
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an interdisciplinary way. Our staff is focused on teaching innovation and research, in which they successfully apply interdisciplinary approach to socially relevant projects with impact. As a faculty, we aim to stay
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Do you want to play a crucial role in developing new applications for the next generation of computer technology? At CogniGron, a globally recognized research center of the University of Groningen
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working groups and committees At the stage of Assistant Professor, 60% of your time is dedicated to research, 30% to teaching, and 10% to organisational tasks. Details about Career Paths in Science and
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an Assistant Professor to strengthen our research and teaching in these fields. As an Assistant Professor, you will develop your own research line, combining theoretical advancements with practical applications
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, including the perspective to get a permanent appointment (tenure) in 1-3 years and become Full Professor in approximately 10 years. As Assistant Professor you will: Set up and develop your own research line
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We are looking for talented candidates who wish to design their own PhD research project on an interdisciplinary topic within the scope of the research expertise of Young Academy Groningen members
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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing