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or expect to complete a PhD in physics or a related field, with strong background in soft matter / nonequilibrium statistical physics / numerical simulation / statistical inference. Scientific skills
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in R or Python Desired - evidence of strong computational skills and large dataset analysis - experience with hierarchical Bayesian modeling - expert knowledge of plant functional ecology - fluency in
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FieldAstronomyYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in astrophysics or a related field. - Experience in data analysis. - Proficiency in Bayesian statistics and nonparametric
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analyses (duration models, difference-in-differences, causal inference methods) •Drafting and co-authoring scientific articles •Contributing to the organization of interdisciplinary events with the ADMI team
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 15 days ago
. Our recent work on inferring parametric information for Chenopodium plants is a starting point for this work [3]. The second network operates on a petal scale, to learn information on the shape and
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Fondation Nationale des Sciences Politiques | Paris La Defense, le de France | France | about 1 month ago
Description Professor Julia Cagé, Principal Investigator of the European Research Council consolidator grant No. 101231066 ECOSOCIAL: Elections, Ecological Inference and Social Capital in Historical Perspective
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from ground or space. The work will consist of continuing to test and process data for inferring information on non-homogeneous aerosol model in the GRASP retrieval algorithm in order to improve
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proposes to cross-correlate thermal proxies, including changes in thermal buoyancies recorded in the elevation history, with existing and newly acquired temperatures inferred from mafic magma reservoirs
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candidate, with a strong background in the development of machine learning methods for bioinformatics. The project focuses on the development of new neural network architectures to perform inference
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/landscape openness/deforestation), sedimentology analysis for reconstruction of past human occupation and pollution, and charcoal analysis for inference of past fire history, metallurgy, and land-use