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Field
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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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his PhD for his thesis on the reception of evolutionary theory in Belgium. Before accepting a position at Maastricht University in 2011, he was a post-doctoral scholar at the University of Leuven and a
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, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
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dynamics at the niche and national levels. Understanding transformative co-evolutionary (social and technical) energy system change necessitates a move beyond mono-disciplinary and interdisciplinary
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national levels. Understanding transformative co-evolutionary (social and technical) energy system change necessitates a move beyond mono-disciplinary and interdisciplinary approaches, which cannot fully
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like contestation, social resistance and conflict disincentivize ECs as do dynamics at the niche and national levels. Understanding transformative co-evolutionary (social and technical) energy system
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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skills and motivation to implement algorithms and test them in practice on large-scale problems. Programming Skills: You are proficient in at least one scientific programming language (such as Python
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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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systems strong analytical and problem-solving skills fluency in English, both written and spoken Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications