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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Deep neural networks for astronomy and satellite imaging applications Where
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Profile The ideal candidate should have: • Knowledge of machine learning, especially neural networks, graph neural networks, or federated learning. • Strong mathematical, optimization, and algorithmic
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attempt to improve one’s own cognitive-affective skills and behavioural performance by means of neuroscience techniques able to influence the activity of neural structures and neural networks supporting
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-aware AI under practical deployment constraints. Familiarity with efficient neural network architectures, including alternative attention mechanisms or mixture-of-experts models. Exposure to trustworthy
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; 2.) use invasive and non-invasive brain stimulation to probe the causal relationship between neural network dynamics and behaviour; 3) leverage these insights to pioneer closed-loop approaches
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; use invasive and non-invasive brain stimulation to probe the causal relationship between neural network dynamics and behaviour; leverage these insights to pioneer closed-loop approaches to therapeutic
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neural network has reduced this cost by a factor of 5000 (Radureau, 2025). This project aims to develop a mesoscopic radiative model to overcome the CFL constraint by adapting the average photon speed
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analysing using quantum-enhanced machine learning. These include strong background both in classical machine learning, physics-informed methods, graph neural networks, and quantum algorithms. The work will
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the foundational mathematics and programming skills necessary for creating basic neural networks and deep learning models from the ground up. Additionally, it is designed for those keen on comprehending
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analysis. The research group focuses on the neural mechanisms of associative learning and cerebellar function. The group is led by associate professor Anders Rasmussen, and currently has 1 PhD student, 1