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on food craving and health-related decision-making. To this purpose, we will use a combination of brain imaging, behavioral measures, and machine-learning techniques. Activities The successful candidate
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analysis, multi-omics analysis, differential analysis, machine learning methods. Definition of tasks to be performed: Fixed-term contract essential to carry out the bioinformatics part of the project
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foundations of feature learning in neural networks. Engage in the life of the CSD, including participating at the weekly seminars. Present results internally and at international venues; Qualifications PhD in
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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results in leading conferences and journals Required Qualifications PhD in one of the following areas (or related fields): Machine learning / deep learning Quantum computing / quantum information Applied
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of the art data science approaches (text mining, machine learning, AI) to comprehensively highlight yet undiscovered virus/host/environment relationships and annotate potentially putative new spillover
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 12 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good