Dear Colleagues, In view of enhancing computation skills in the geographic domain, Spatial Ecology <http://spatial-ecology.net/> is organising a two-month training course: Geocomputation and Machine Learning for Environmental Applications <http://spatial-ecology.net/course-geocomputation-machine-learning-for-environmental-applications-intermediate-level-2023/> .
The course will be offered on-line with a supplementary 5-day (or 10-day) in-person segment at the University of Basilicata, in the magnificent town of Matera <https://www.google.com/maps/place/75100+Matera,+Province+of+Matera,+Italyemail@example.com,16.5651092,13z/data=!3m1!4b1!4m5!3m4!1s0x13477ee2482b152b:0x8f6a4ae10da9360!8m2!3d40.666379!4d16.6043199>, Italy. This is a wonderful opportunity for PhD students, Post-Docs and professionals to acquire advanced computational skills with a Linux computer. Please forward to announce this opportunity within your network. Sincerely, Giuseppe Amatulli & Spatial Ecology – Team *Geocomputation and Machine Learning for Environmental Applications <http://spatial-ecology.net/course-geocomputation-machine-learning-for-environmental-applications-intermediate-level-2023/>.** (April, May, June, 2023)* In this course, students will be introduced to an array of powerful open-source geocomputation tools and machine learning methodologies under Linux environment. Students who have never been exposed to programming under Linux are expected to reach the stage where they feel confident in using very advanced open source data processing routines. Students with a precedent programming background will find the course beneficial in enhancing their programming skills for better modelling and coding proficiency. Our dual teaching aim is to equip attendees with powerful tools as well as rendering their abilities of continuing independent development afterwards. The acquired skills will be beneficial, not only for GIS related applications, but also for general data processing and applied statistical computing in a number of fields. These essentially lay the foundation for career development as a data scientist in the geographic domain. More information and registration: www.spatial-ecology.net twitter: @BigDataEcology -- Giuseppe Amatulli, Ph.D. Research scientist at School of the Environment Yale University New Haven, CT, USA 06511 Twitter: @BigDataEcology Teaching: http://spatial-ecology.net Work: https://environment.yale.edu/profile/giuseppe-amatulli/ [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo