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Field
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. Skills in modelling and analysis using machine learning tools. Experience in environmental and economic assessment and in feasibility and replicability studies. Scientific production (Q1) and technical
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the social and environmental implications of proximity models, combining spatial, mobility and socioeconomic data to develop indicators and tools for the toolkit. The candidate will also assume coordination
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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sustainability of biomaterial manufacturing through safe design methods, machine learning, and predictive life cycle assessment as well as developing machine learning and hybrid digital modeling methods, combining
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LevelPhD or equivalent Skills/Qualifications The selected candidate will perform the following duties: - Experimental: generation and management of organoid models derived from patients, performance of high
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offer: Support in preparing your application. Scientific supervision by senior researchers with expertise in ageing biology, behavioural analytics, neurogenetics and Drosophila melanogaster as a model for
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techniques to create a model for the early prediction of victimization, offending and the victim-offender overlap, which could be employed in school and residential contexts to identify youth in urgent need
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Infrastructure? No Offer Description Research line: Learning in single cells through dynamical internal representations. Job description: Develop theory, models and algorithms for identifying molecular encodings
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, - Participation in national/international conferences, - Direction of master's or undergraduate students, - Participation in science engagement activities, - Certification to work with animal experimental models
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Experience in advanced statistical analysis models Experience working with large imaging datasets Experience in advanced MRI sequence and analysis Ability to work well in a multidisciplinary team