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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 1 month ago
. Test model predictions in behavioral experiments. Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems
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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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properties of macromolecules, developing novel ways to combine quantum chemical methods and machine learning, developing quantum algorithms for computational chemistry on quantum computers, and applying
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pipelines and algorithms to construct and evaluate foundation models for whole-body and abdominal MRI. Alongside this, you will conduct comprehensive and systematic literature and database searches related
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Duration: Initial appointment is for 1 year, with extensions (up to a total of 3 years) contingent upon performance Salary range: $80,000-$100,000 Apply here: https://jobs.ashbyhq.com/episteme/347d9b19-be92
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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