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, as well as doctoral programmes and several Master’s programmes. Open to students in initial education as well as to professionals through lifelong learning, these programmes are delivered by our three
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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, GC-MS/MS, and advanced NMR approaches. - **Activity 4:** Multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and reveal the interactions linking
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-toulouse.fr/cofund-best https://adum.fr/as/ed/proposition_Setab.pl Street 118, Route de Narbonne Postal Code 31062 E-Mail [email protected] STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
learning (noise robustness, statistical generalisation) with those of symbolic AI (explainability and logical reasoning). The objective of this thesis is to study the contribution of neuro-symbolic
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
learning, privacy, security and distributed systems. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-10411 Requirements Skills/Qualifications We are looking for a candidate with
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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€2,300 Contract duration: 3 years Contract date from 01/12/2026 to 30/11/2029 Where to apply E-mail [email protected] Requirements Research FieldEconomicsEducation LevelMaster Degree
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remaining biologically interpretable? The PhD candidate will design and apply integrative computational workflows using methods such as multi-omics integration, spatial modelling, representation learning
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be