Transform data into solutions! 

Scope of the training

Climate change exerts significant pressure on water resources. With the advent of the Internet of Things (IoT), innovative remote sensing techniques, and new data processing technologies, a wealth of new big data has become available. This data can be utilized to enhance our understanding of the water and climate system, leading to the development of more sustainable and climate-resilient water management strategies. However, the utilization of big data in climate science and water science poses various scientific challenges that require attention from researchers and scientists. These challenges encompass data volume and variety, data quality and uncertainty, data integration and interoperability, computational issues, data privacy and security, as well as data analytics and modeling. Addressing these scientific challenges associated with big data in climate science and water science is essential, as it will enable researchers to delve deeper into insights, improve predictive models, and contribute to the enhanced management and understanding of the water and climate system.

In this winter PhD class, students will be introduced in the most recent issues, technology and case studies related to the use of Big Data in water and climate sciences.  The winter class will combine theoretical lectures, with hands-on exercices in computer class rooms. 

Approach

In this PhD class, 7 professionals will give guest lectures on the use of advanced data science techniques applied to climate and water sciences. The lectures will use a mixed approach, combining theoretical exposure combined with hands-on exercises in the computer room. Use will be made of Open Science approaches, using Opens Science software and data.

Each PhD student will also have the possibility to present his current PhD work using a poster and short oral presentation.

Target group

-          On site: 24 PhD students (first to second year) working in the area of environmental, hydrological or climate modeling using either process-based or data-driven approaches, or a combination of both.

-          Online: 50 PhD students working in the area of environmental, hydrological or climate modeling using either process-based or data-driven approaches, or a combination of both.

Prerequisites

-          The student must be enrolled as a PhD student in one of the universities belonging either to the ENVITAM or Circle U consortium

-          The student must have a PhD subject related to climate or water science

-          The student must have basic skills in data analysis, preferably with open software (Python, R, …)

-          The student must be fluent in English communication.  

Learning outcome

-          The PhD student will discover and apply a set of new advanced data science techniques applied to climate and water science.

-          The PhD student will develop his scientific communication skills during the oral and poster presentation.

Simultaneously, the training will be accessible via Teams using the following link:  https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_OTZkYjhiZmEtZWViMS00OTQxLTg0NDEtOGJjMWQxNzBhY2Ez%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%25227ab090d4-fa2e-4ecf-bc7c-4127b4d582ec%2522%252c%2522Oid%2522%253a%25221d553ee0-d0d6-40a1-b7ea-e14aea5fc804%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=be172c5d-f7cf-4d09-baf9-25d487667611&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true

Program update (28/1/25)

Registration, participation fees, certification,  and registration deadlines.

The training is limited to 24 PhD students on site.

The training is limited to 50 PhD students via Teams.  

Registration fees are waived.  During the meeting, UClouvain, Circle U, and ENVITAM provide catering (coffee, sandwiches). However, the organization does not provide overnight rooms or travel support. 

Participants will receive a certificate of attendance. 

• Participation on site, will be credited by 3 (attendance) + 1 (short presentation) ECTS

• Participation via Teams, will be credited by 3 (attendance) ECTS

Support material

Recorded sessions