Publisher
source

Suraj Bhagat

2 months ago

PhD Opening in Geospatial Science, AI/ML, and Hydrologic Modeling SRM University, AP in India

Degree Level

PhD

Field of study

Computer Science

Funding

Available

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Country

India

University

SRM University-AP

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Keywords

Computer Science
Machine Learning
Environmental Science
Deep Learning
Remote Sensing
Geography
Hydrology
Artificial Intelligence
Civil Engineering
Earth Science
Python Programming
Uncertainty Analysis
Data Assimilation
Transformer Technology
Geographical Science
Statistics

About this position

Professor Suraj Bhagat at SRM University, AP is recruiting PhD candidates for research on geospatial science and AI/ML across multiple projects, with a focus on related hydrologic parameters and water-resource challenges.

The research themes include GIS, remote sensing, machine learning, deep learning, time-series modeling, LSTM, Transformer models, ensemble methods, data assimilation, and uncertainty quantification. The post also highlights practical experience with Python, R, and cloud/GIS tools such as Google Earth Engine, QGIS, and ArcGIS.

This opportunity is best suited to candidates interested in environmental science, earth science, geography, and computational approaches to climate intelligence and environmental data science. The announcer describes the ideal applicant as bright and driven, with strong technical skills and a passion for real-world water-resource applications.

To apply, email a CV and a brief research statement to [email protected]. No deadline is stated in the post.

Funding details

Available

What's required

Applicants should have strong expertise in geospatial data analysis (GIS, remote sensing), proficiency in machine learning/deep learning methods including time-series models, LSTM, Transformer, and ensemble methods, coding skills in Python and R, experience with data assimilation and uncertainty quantification, and familiarity with cloud/GIS platforms such as Google Earth Engine, QGIS, and ArcGIS. The post is aimed at bright, driven candidates interested in applying AI to water-resource and environmental research.

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