Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine...
The scientific programme covers topics such as: introduction to sensitivity analysis and overview of techniques, uncertainty analysis, sensitivity analysis, graphical methods, derivative-based measures, high dimensional model represent ations, variance-based and screening methods, sensitivity analysis with given data, sensitivity measures for correlated inputs, practicum, exercises for students, and round table with selected case studies from students.
A preliminary list of instructors is: Andrea Saltelli ((UPF Barcelona School of Management - University of Bergen), William Becker (former JRC), Stefano Tarantola (JRC), Rossana Rosati (JRC), Thierry Mara (JRC, University of Reunion), Ivano Azzini (BriLeMa N.P. Association), Marco Riani (University of Parma), Sergei Kucherenko (Imperial College London), Elmar Plischke (Clausthal University of Technology).
The school will be organised remotely, therefore, the selected participants can follow the lectures from their place.
The school is free (no fee).
The school can accommodate up to 25 participants. Candidates will be selected based on their CV and, clarity and relevance of the motivation letter. The following criteria will be taken into account: priority to PhD students and researchers whose ongoing research requires sensitivity analysis, gender balance, candidates with at least basic knowledge in statistics and programming skills.
To apply fill in the application form providing your short CV and a motivation letter (in a single pdf file) by 30 April 2022: https://ec.europa.eu/consultation/runner/samo-summer-school
The deadline for submitting application is 30 April 2022. The organising committee will not accept late candidatures. Successful candidates will be informed by email no later than 10 May 2022.
The morphological spatial pattern analysis derived from the Forest/Non-Forest Map 2000 (FMAP2000) using the MSPA algorithm at a spatial resolution of 25-m. Further details available in...
The morphological spatial pattern analysis derived from the Forest/Non-Forest Map 2006 (FMAP2006) using the MSPA algorithm at a spatial resolution of 25-m. Further details available in...