226 algorithm-development-"The-University-of-Edinburgh" Postdoctoral positions in France
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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within
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nonstationary models and algorithms for analyzing various biological signals. The project will focus mainly on developing innovative models for biomedical signals with irregular cyclicity and exploring potential
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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the necessary data from numerical models and observations to build the dataset; Identify the algorithms best suited to learn the targeted behaviors; Train the learning models; Validate their ability to predict
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: Computer science MAIN SUB RESEARCH FIELD OR DISCIPLINES1: Distributed Algorithms – Fault Tolerance – Cloud computing JOB /OFFER DESCRIPTION We are looking for a young researcher interested in fault tolerance and
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components: - operational modal analysis to extract the modes of the probed medium, - algorithmic and experimental developments on the MSE method - and algorithmic and experimental developments on the MFP
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· Carry out research activities under the Department's contracts in the RS2M field · Participate in and ensure the delivery of project deliverables · Design and develop models, algorithms, and
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 4 hours ago
dedicated Engineers responsible for the actual implementations. Hence, your primary focus will be on the creation of sound algorithms and methods, ensuring their theoretical integrity and applicability
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learning, and generative AI Design and implement algorithms for quantum-inspired and quantum-enhanced generative models Investigate theoretical foundations of tensor networks, entanglement, and collapse