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Field
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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
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Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications of technology Contributions to open source projects This is what we offer A temporary contract for 38
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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 for computer vision
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or multiple fields relevant to the Evolve Doctoral Programme, such as (bio)chemistry, (bio)physics, molecular biology, computational science, systems biology, evolutionary biology, ecology, astrophysics