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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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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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that enhance the development and evaluation of advanced analytical models using health data. This includes methods for prediction, explainability, prediction under intervention, algorithmic fairness, transparent
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uplinking a consolidated set of payload and platform operations requests. Candidates interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic
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teaming for exploration. Candidates interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Optimisation for Robotic Mobility
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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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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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. Further details on the CHIME mission can be found at: Candidates interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship
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(FELIX) for ATLAS detector systems. The group also has a strong record in track reconstruction, flavour tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics
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algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse LDES performance under different grid scenarios. Collaborate with consortium partners to translate