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surrogate models. The method employs modern posterior sampling algorithms, using measurement data. The developed tools will enable faster and more accurate reconstruction of past and future emergency events
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different research areas. The more details can be kindly found as below: 1. Industrial AI Algorithm and Autonomous Decision-Making Systems RESPONSIBILITIES Research and develop autonomous decision-making
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approaches, such as topology optimization, with gradient-free methods, such as genetic algorithm, in order to exploit the specific strengths of the two classes of algorithms, and through this reach an ideal
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advantage may arise. The focus of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances
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learning algorithms and creating predictive models. This role will contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data as well as develop
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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant programming languages (e.g
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systems, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 26 days ago
unified understanding of behavioral adaptation. The project aims to: Develop and analyze computational models that capture flexible, multi-timescale learning and adaptation in recurrent neural circuits
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Description Evaluation of the robustness of ML algorithms executed on a non-hardened accelerator through circuit-level fault-injection The project addresses the challenges of deploying AI applications