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Université de Lorraine/Laboratoire LAE UMR1121 | Vand uvre les Nancy, Lorraine | France | 7 days ago
. Experience in: background in elucidating the structures of natural products using 1D and 2D NMR and HRMS, molecular network data processing and metabolomic statistical approaches, development and validation
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through the EU Research Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Researcher in statistical modelling and
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. - Experience with high-throughput sequencing, metagenomics or metatranscriptomic analyses of large-scale datasets. - Experience of data analysis, statistics and visualisation, for example in Python or R
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17 Apr 2024 Job Information Organisation/Company ECOLE NORMALE SUPERIEURE DE LYON Research Field Mathematics » Applied mathematics Mathematics » Statistics Researcher Profile Recognised Researcher
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to develop tools for monitoring clinical activities; - Ability to transmit information using appropriate media for different audiences. - Basic knowledge of database management / statistics - Knowledge
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, biostatistics, and infectious diseases. The position includes leading statistical analysis and manuscript writing in collaboration with the research team. The selected candidate will be encouraged to present
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-team.org/ https://georessources.univ-lorraine.fr/ https://ensg.univ-lorraine.fr/ The ideal candidate is proficient in Geostatistics, Statistics, Geomodeling, Programming (Python), Reservoir modeling in
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in molecular simulation is desirable, as well as a good knowledge of associated theoretical tools (statistical physics, etc.; programming experience including: Python, Fortran, C, C++, etc.). A good
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with a strong background in statistics, machine learning, computational systems, biology or physics. The successful candidate should have experience in high-throughput data analysis in biology. Ideally
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program will serve as a PhD track. Teaching at Master's and Doctoral level will focus on the advanced quantitative methods: survey techniques, statistical processing of big data, experimental methods