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Ref. 4364_All. 3 - Methods and Tools for Secure and Autonomous Brain-Inspired Cyber-Physical Systems
. The neuromorphic component will address the design, optimization and deployment of Spiking Neural Networks (SNNs) and event-based algorithms on embedded processors and specialized accelerators, including data from
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are not limited to linear discriminants, neural networks, decision trees, support vector machines, unsupervised learning, and reinforcement learning. With examples from real-world applications, students
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. The position in Heilbronn is already filled. This job description is for a position in Garching . Here, you will develop general-purpose, scalable linear solvers for training neural networks that represent
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-level approach, from genes and molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139 Where to apply E-mail [email protected] Requirements Research FieldPsychological
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molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139 Where to apply E-mail [email protected] Requirements Research FieldNeurosciencesEducation LevelMaster Degree
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opportunities. The chance to help shape a newly funded research line with real clinical impact, within an international network of academic and industry partners. Where to apply Website https
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI
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), the Research Unit FOR 5289 (From Imprecision to Robustness in Neural Circuit Assembly), or the Research Training Groups 4R (R-Loop Regulation in Robustness and Resilience) and GenEvo (Gene regulation in
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Experience (Required) Programming Experience in Large Learning Models and Neural Networks (Preferred) 1. Knowledge of sterile techniques used in tissue culture. (Required) 2. Knowledge and skill to manage
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of uncertainty in reward-guided decision-making. 2024.03.27.587016 Preprint at https://doi.org/10.1101/2024.03.27.587016 (2024). 6. Walker, E. Y. et al. Studying the neural representations of uncertainty. Nat