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stability (beam break-up). The Grenoble Laboratory of Subatomic Physics and Cosmology (LPSC) ( http://lpsc.in2p3.fr ) is a mixed research unit associating CNRS-IN2P3, Grenoble Alpes University (UGA) and
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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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on the design and performance analysis of resource allocation algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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platform integrating advanced robotics, materials characterization systems, digital twins and AI-based decision-making algorithms for accelerated research. The platform will incorporate explainable AI
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 14 days ago
. Main activities : – Read papers and state of the art - Benchmark existing algorithms – Write problem formulation, proofs of convergence. – Adapt the formulation to the target scenario. – Propose a new
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, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies
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dielectric films deposited on graphene using a non-contact microwave technique ( https://dx.doi.org/10.1021/acs.jpcb.9b11622) and monolayer graphene ( https://dx.doi.org/10.1021/acs.jpcb.9b11622 ) as a