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compressed sensing et la capacité à optimiser des algorithmes pour des applications temps réel, qui seront également valoriséesThe candidate should have: - A strong background in applied mathematics, including
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fait appel à des « pipelines » d'analyse qui associent les technologies de séquençage innovantes aux algorithmes bioinformatiques pour assembler, annoter et comparer les génomes viraux. Le projet
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
of this project requires the design, development, and training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus
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on the aptitude of the candidate(s), the project could be oriented on the instrumentation and the development of signal processing algorithms, time series data processing and modeling, statistical analysis and
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" setting [4], where the benchmark is the optimal online algorithm rather than the expected maximum, making the competition more dynamic. - Study settings where multiple items are allocated to buyers, such as
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work will be organized around the following areas: 1. Bee detection and tracking: Development of computer vision algorithms to identify and track each bee from high-resolution images, while
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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candidates may be asked to teach: Introductory programming classes Core undergraduate CS classes such as: Human Computer Interaction, Database Applications, Algorithms and Data Structures, Software engineering
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems