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Field
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. The objective of this postdoctoral project is to develop a unified, AI-compatible framework for non-neural behavior based on dynamical systems and learning. Behaviors will be modeled as low-dimensional dynamical
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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. The PhD will employ a combination of simulation and experimental validation. First, use and develop existing coronagraphic simulation tools in python to develop innovative algorithms, then conduct tests
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will be involved in the development of MKID detectors. Close interaction with the teams involved in the AMINO project is also necessary in order to properly define the algorithms required for data
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algorithms (such as machine learning or clustering). Familiarize with the multi-level data and how to model them in a polystore architecture [2] or similar (Month 1 – 12). Develop an environment to test the
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networks are not well-suited to the computational constraints of FHE. The project aims to develop more efficient neural network architectures tailored for encrypted computations. The postdoctoral researcher
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atmospheric sciences • Knowledge of cloud or aerosol physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a
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epitaxial interfaces, and can be made free of nuclear spins that would interfere with the electron or hole spins. It is essential to support the development of these advanced quantum technologies with state
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, funded by the ANR P2S2 project. The position is available initially for a fixed-term duration of 2 years, with the possibility of extension for 1 further year. The P2S2 project aims at developing parton
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