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                Application Deadline 24 Sep 2026 - 09:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to 
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                Application Deadline 29 Aug 2026 - 01:06 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to 
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                The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg National Research Fund. MICRO-PATH, or Pathogenesis in the Age of the Microbiome (https 
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                . Activities of the successful candidate: Conduct extensive background literature analysis. Plan and organise experiments to define and test hypotheses and develop forefront research. Publish the results 
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                interannual variability of spectral signatures in different bands (Planet, Sentinel-3, Sentinel-2, ECOSTRESS, Landsat, SMAP), which will be linked to LUE-WUE and gc information from multiple eddy covariance 
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                -data integration, data analysis and working with HPC. Scripting and programming experience are essential. · You like working in a team, explaining and presenting research ideas and results 
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                · Programming skills in Matlab, R, and/or Python · Experience in the organisation of field campaigns Language skills · Fluency in English, both oral and written. Your LIST benefits 
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                Application Deadline 20 Aug 2026 - 09:44 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to 
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                processes, · You enjoy both modelling, model-data integration, data analysis and working with HPC. Scripting and programming experience are essential. · You like working in a team, explaining 
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                extended from cloud solutions (such as OpenLLMetry), the research question is how to identify anomalies in collected information that can come from multiple AI services either invoked manually by users or by