Senior Research Associate in Data-Driven Weather and Climate Modelling (Fixed Term)

University of Cambridge; Cambridge; On-site; GBP 30,000 - 45,000; Full time

Job summary

A leading UK university is seeking an outstanding scientist for a one-year research fellowship in machine learning and earth sciences. Responsibilities include conducting research, managing budgets, and contributing to teaching programs. Candidates should have a PhD in a relevant field and at least three years of research experience. The role offers research expense support and the opportunity for collaboration and publications.

Qualifications

• PhD in a relevant area of earth sciences drawing on techniques from machine learning, mathematics, statistics, or data science.
• Research experience in data-driven techniques in numerical weather prediction and/or climate forecasting.
• Normally at least three years' research experience obtained in academic or industrial settings.

Responsibilities

• Define, develop, and conduct individual and collaborative research objectives.
• Manage research budgets and secure funding.
• Contribute to teaching programs and supervise postgraduate research students.

Skills

Data-driven techniques

Machine learning

Statistical analysis

Research experience

Education

PhD in relevant area

Job description

Overview

Recent advances in machine learning have opened up a variety of data-driven modelling techniques within science which are showing unprecedented abilities to replace traditional models with fast approximations. Numerical Weather Prediction (NWP) is one area where machine learning based models are showing comparable skill with traditional techniques at significantly reduced computational cost. Data-driven techniques can cover entire forecasting pipelines, in some cases obviating the need for state estimation and operating directly on observations. Further work is needed to develop models usable in operational contexts, integrating with observations across different scales, and predicting extremes, seasonal variability, localised forecasts, and more. There remains a gap between weather and climate modelling in this space; further work is needed to understand whether and how techniques that work well for machine-learned NWP can be applied to longer timescales for climate predictions. These are the broad areas of interest for the present position based in the Institute of Computing for Climate Science.

The institute is a joint venture between the Department of Applied Mathematics and Theoretical Physics, the Department of Computer Science and Technology, and University Information Services, supported by philanthropic and grant funding. ICCS aims to advance weather and climate modelling through a multi-disciplinary approach, drawing on earth sciences, mathematics, statistics, software engineering, computer science, and machine learning.

Duties and Opportunity

This position offers a unique opportunity for an outstanding scientist with expertise across data science, computer science, machine learning and earth sciences to carry out foundational research. The fellow will be based in DAMTP and affiliated with Queens’ College. This is a one-year appointment in the first instance that includes research expenses support, with the possibility for the SRA to apply for additional funding as principal investigator. As part of the University of Cambridge, ICCS contributes to education and training and to Cambridge Zero, the University’s climate change initiative for creating a sustainable, zero-carbon future.

Duties include defining, developing and conducting individual and collaborative research objectives, proposals and projects, and managing research budgets. You are responsible for the investigation and delivery of your own research programmes, and for assessing, interpreting and evaluating outcomes. You are expected to extend and apply knowledge, contribute to publications, present and communicate complex ideas to those with limited knowledge. You should identify funding sources, help secure funds and develop links with external contacts. You may contribute to teaching programmes, supervise postgraduate research students, mentor colleagues and carry out appraisals.

Qualifications

• PhD in a relevant area of earth sciences but drawing on techniques from machine learning, mathematics, statistics, or data science
• Research experience and expertise in data-driven techniques in numerical weather prediction and/or climate forecasting
• Normally at least three years\' research experience (obtained in either academic or industrial settings)
• A strong track record in one or more relevant research areas
• Evidence of high-quality research outputs
• Evidence of potential for collaborations

Application Process

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online. Please indicate the contact details of three academic referees on the online application form and upload a full CV, publications list, a description of your recent research, current research and future research interests (not to exceed two pages). Ensure at least one referee can be contacted during the selection process and be aware that they may be asked to upload a reference for you to our Web Recruitment System.

If you have any questions regarding the role or the application process, please contact: Colm-cille P. Caulfield ([e-mail]) and Dominic Orchard ([e-mail]).

If you have any queries regarding the application process, please contact [e-mail].

Equal Opportunity and Eligibility

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from women and/or candidates from a BME background for this vacancy as they are currently under-represented at this level in our Department. The University has a responsibility to ensure that all employees are eligible to live and work in the UK.