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' button. E-mail applications cannot be accepted. Include the following information in your application: a letter of motivation, using the attached template ; your curriculum vitae, including any courses
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the time-to-market of innovations while ensuring they are implemented responsibly to address urgent societal challenges, such as climate change. In the past decade, innovation policy has witnessed a
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of data structures, static analyses and compiler optimizations, parallelism and concurrency) to turn these new theoretical developments into performant implementations; building state-of-the-art
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nanoscale materials and gas-phase processes while working as part of an international team. We will require that you follow safe and responsible work behavior both in the laboratory and outside it. If you are
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online procedure using the 'apply now' button. E-mail applications cannot be accepted. Include the following information in your application: your curriculum vitae, including any courses taken
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
for mapping future decarbonization pathways. Building on these insights, you will explore policy portfolios that are responsive to emerging tipping dynamics, assessing their trade-offs. Your key tasks will
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to address the questions in one of the two projects. This will include specifying the research questions and expectations, designing the relevant studies and instruments, collecting and analysing data, and
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this position aims to achieve automated computational design synthesis of system topologies. This will result in new and novel system topologies which can be automatically analysed and evaluated based
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of hyperbolic deep learning and one PhD student with a keen interest in the algorithmic side of hyperbolic deep learning. Tasks and responsibilities: Conduct high-impact research on hyperbolic deep learning
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and early intervention strategies. Your responsibilities will include: Developing and validating a machine learning prediction model for CP and related outcomes using neonatal MRI, EEG, GMA, HINE, and