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Field
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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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
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(UNCG). JSNN builds on the strengths of the universities to offer innovative, cross-disciplinary graduate programs in the emerging areas of Nanoscience and Nanoengineering. Minimum Requirements PhD in
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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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AI Algorithm and Autonomous Decision-Making Systems RESPONSIBILITIES Research and develop autonomous decision-making algorithms for process industries (e.g., chemical, pharmaceutical, materials
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quantum dots. In particular, based on our recent result in small arrays, we aim at exploiting coherent control of spin qubit in larger arrays to perform quantum simulation or algorithm. Therefore
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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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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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ovarian cancer prognosis, COVID-19 prediction, and childhood mental health. You will have (or be close to completing) a PhD in a quantitative discipline such as computer science, mathematics, statistics