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both theoretical challenges and practical applications. Key Responsibilities: Develop innovative methods and tools for adaptive, AI-driven embedded algorithms to support real-time analytics and decision
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
, located at Inria Paris and École Normale Supérieure (ENS). The team conducts research in various aspects of quantum information theory, including quantum error correction, quantum algorithms, and the
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of pediatric cancer patients and discover the impact of therapeutic exposure on development of secondary neoplasms in adult survivors. Position Responsibilities: Evaluate bulk-tumor deconvolution algorithms
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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on using unstructured and overset meshes with high-fidelity algorithms to obtain scale-resolved data. Candidate will also post-process data using data-driven and physics-driven methods to extract fundamental
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
development of data processing tools. The position is for one year with the possibility of reappointment for an additional year. It will involve developing, implementing, and validating novel algorithms
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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funded by a EU programme Reference Number 304--1-14162 Is the Job related to staff position within a Research Infrastructure? No Offer Description Join a research team developing state-of-the-art open
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at SLATE. The position is for a fixed term of four years, with the inclusion of career development work (e.g., teaching, project work) accounting for 25% of the entire period, and is financed by University
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation