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and train deep learning models (CNNs, ...) data to predict IPLSs from multi-omics datasets (epigenomics, transcription, sequence). • Using representation learning (tensor factorization framework e.g
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: - Methodology development in wavefunction-based electronic structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods, etc. - ML-augmented numerical method
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( https://www.i3s.univ-cotedazur.fr ) is a joint research unit between the CNRS and the Université Côte d'Azur with Inria as secondary regulatory authority. i3S was one of the first laboratories to be
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objectives: • Microscopic modeling of optical conductivity: develop a realistic description of the frequency-dependent conductivity tensor σ(ω) using numerical approaches applicable to a wide class of
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dynamical problems by tensor networks and neural networks. In all these cases, the parametrization is typically irregular, meaning that the occurring linear systems are generally ill-conditioned. The
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-dimensional streaming tensor data for latency sensitive and real-time applications. Low-SWaP, high-performance embedded computing (HPEC) High-speed, low-latency data interfaces and networking Real
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of human brain imaging data, including functional MRI (fMRI) and diffusion tensor imaging (DTI), and a strong interest in translational neuroscience. Our research aims to identify reproducible brain
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tensor programming Applications to physical sciences, dimensional analysis, or climate impact modeling It is highly meritorious if the doctoral degree has been obtained within the last three years prior
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your ranked top choices of courses for the Winter 2027 semester by uploading the completed TA application form to your application in Workday: https://www.mcgill.ca/hr/files/hr/1_
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needed to advance the field is fundamental to the work of the center. Research projects currently pursued at CCQ cover a broad range of methods and interest, see: https://www.simonsfoundation.org