Lead AI Researcher for Global River Dynamics

Corehr; Oxford; On-site; GBP 50,000 - 70,000; Full time

Benefits offered by this job

38 days annual leave

Childcare services

Family leave schemes

Discounted bus travel

Job summary

The University of Oxford's School of Geography and the Environment invites applications for a Senior Researcher to lead the technical delivery of EARTHFLOW-AI: Global River Dynamics via Artificial Intelligence, a NERC-funded project building a global, AI-ready river data infrastructure.

You will develop scalable data and ML pipelines, upgrade the GRIT hydrography, integrate SWOT data, and evaluate models from baseline to graph neural networks, while coordinating open data releases and

Qualifications

• Holds a relevant PhD/DPhil with post-qualification research experience and a strong lead-author publication record.
• Excellent Python and research software engineering skills with experience in large-scale geospatial datasets and high-performance or cloud computing.
• Experience leading research projects, managing budgets and coordinating or supervising others.

Responsibilities

• Lead the technical delivery of EARTHFLOW-AI: Global River Dynamics via Artificial Intelligence.
• Develop scalable data and ML pipelines; upgrade GRIT hydrography and integrate SWOT data.
• Design and evaluate models from baseline approaches to graph neural networks.
• Oversee open release of project datasets, models, code and documentation.
• Travel to partner organisations and international conferences.

Skills

Python

Machine learning

Geospatial analysis

Research software engineering

Leadership

Education

PhD in geography / earth observation / ML

Tools

GRIT hydrography

SWOT data

Job description

The University of Oxford's School of Geography and the Environment invites applications for a Senior Researcher to lead the technical delivery of EARTHFLOW-AI: Global River Dynamics via Artificial Intelligence, a NERC-funded project building a global, AI-ready river data infrastructure.

You will develop scalable data and ML pipelines, upgrade the GRIT hydrography, integrate SWOT data, and evaluate models from baseline to graph neural networks, while coordinating open data releases and