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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
to significantly accelerate computations while maintaining sufficient physical accuracy for geoscience applications. Inspired by methods from computer graphics, this approach could enable unprecedented resolutions
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a
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, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results into useful decision support Publish and communicate results and
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join the internationally recognised EnDROIDS programme (https://endroids.ico2s.org ) and become part of a highly interdisciplinary team of computer scientists, molecular biologists, DNA nanotechnologists
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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law
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of novel CAR-T cell therapies targeting pediatric central nervous system tumors, with a specific focus on rhabdoid tumors (AT/RT). The candidate will work under close supervision within a multidisciplinary
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insights for streaming, broadcast, accessibility and media production. Candidate profile Applicants should have a background in machine learning, audio engineering, speech processing or a related discipline