[R-sig-Geo] Course: Geocomputation and Machine Learning for Environmental Applications -2023

[Giuseppe Amatulli]

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,+Italy/@40.6646012,16.5651092,13z/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/

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