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Analyze and preprocess large-scale spatiotemporal climate datasets.
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Develop and implement deep learning models for climate anomaly prediction.
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Apply architectures such as CNN-LSTM, ConvLSTM, and Transformer-based models to real-world climate problems.
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Evaluate model performance using appropriate forecasting and anomaly detection metrics.
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Design AI-driven frameworks for early warning systems and climate risk analytics.
Mentor Based
Spatiotemporal Deep Learning for Climate Anomaly Prediction
Learning climate dynamics with spatiotemporal neural networks.
About This Course
Climate anomalies such as heatwaves, extreme rainfall, droughts, cyclones, and unexpected seasonal shifts are increasing in frequency and intensity. Accurate prediction of these events requires models that understand both spatial relationships (geographic patterns) and temporal dynamics (time evolution) of climate data.
This advanced workshop focuses on Spatiotemporal Deep Learning techniques for predicting climate anomalies using modern AI architectures. Participants will learn how to build models that capture complex climate patterns across space and time using neural networks designed specifically for geospatial and time-series data.
Aim
The aim of this workshop is to equip participants with the theoretical understanding and practical skills required to design, implement, and evaluate spatiotemporal deep learning models for accurate climate anomaly prediction. The program focuses on integrating climate science with advanced AI techniques to enable early detection of extreme events and support data-driven climate risk assessment and decision-making.
Workshop Objectives
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To introduce the fundamentals of climate anomalies and spatiotemporal data analysis.
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To develop understanding of deep learning architectures (CNN, LSTM, ConvLSTM, Transformers) for climate prediction.
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To train participants in handling real-world climate datasets (NetCDF, satellite, reanalysis data).
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To build and evaluate spatiotemporal models for forecasting extreme climate events.
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To enable participants to design AI-driven early warning and climate risk assessment systems.
Workshop Structure
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/27/2026
IST 4:30 IST
Workshop Dates
02/27/2026 – 03/01/2026
IST 5 :30 PM IST
Workshop Outcomes
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
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