Senior Research Associate in Hydroclimatic Extremes and Causal Pathways

Overview

As a Senior Research Associate, you will lead analyses within the EXTREME-FUTURES project’s Work Package 1 on Causal Pathways, focusing on extreme and compound hydroclimatic events. You will collaborate with international partners to connect climate drivers, weather regimes, and atmospheric features, developing statistical and AI/ML methods to identify semi-predictable pathways. The work begins in Europe and expands globally, contributing to a seamless weather-to-climate framework. This role offers impact through advancing understanding of hydroclimatic extremes and cross-disciplinary collaboration.

Pay / Benefits

• generous holiday package
• pension schemes
• health and wellbeing initiatives
• opportunity to buy more holidays
• supportive career development
• adult learning opportunities

Responsibilities

• Assess global temperature and precipitation anomalies to identify extreme events across scales
• Investigate causal pathways linking climate drivers, circulation regimes, and atmospheric features in Europe and beyond
• Develop and apply statistical and causal-inference approaches to quantify driver–feature interactions
• Collaborate with partners to create and evaluate AI/ML algorithms for semi-predictable pathways and benchmark against forecast systems
• Lead integration and dissemination of WP1 outputs within the project framework
• Contribute ideas to enhance technical/methodological aspects of the project
• Determine appropriate research methodologies and scope for follow-up investigations
• Contribute to writing, presentations, and publications; support grant applications and new proposals

Key requirements

• Knowledge of climate science, atmospheric science, meteorology, hydroclimatology, or related quantitative disciplines
• Experience analyzing large observational, reanalysis, climate-model, or weather-forecast datasets across scales
• Strong programming skills (Python, R) and reproducible workflows
• Experience with statistical analysis of climate variability and extremes; desirable: causal inference, weather regimes, feature tracking, or compound-event analysis
• Experience or ability to develop AI/ML methods for environmental data
• Collaborative and effective working with methodological specialists
• Attention to detail and high accuracy in work
• Strong communication and presentation abilities
• Proven ability to analyze data and publish results
• Experience of working collaboratively in teams
• Excellent IT skills across major office applications
• Initiative and creativity in solving research problems
• Awareness of the research environment
• Analytical and problem-solving capability
• Conference and high-quality publication experience

• collaboration
• attention to detail
• clear communication
• Python
• R
• statistical analysis

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