met-jobs@lists.reading.ac.uk
January 2021
Message 63

[Periods|Index by:DateThreadSubjectAuthor|Date:PreviousNext|Thread:(Previous)(Next)|List Information]

[Met-jobs] Oxford University Postdoc in Machine Learning for Climate

From Hannah Christensen <hannah.christensen@physics.ox.ac.uk>
To "met-jobs@lists.reading.ac.uk" <met-jobs@lists.reading.ac.uk>
Cc Hannah Christensen <hannah.christensen@physics.ox.ac.uk>
Date Tue, 19 Jan 2021 11:24:57 +0000

Dear MetJobs list users,

Please find below details of an opening in the Atmospheric Processes Group in the Department of Physics, University of Oxford.

*****
Postdoctoral Research Assistant in Machine Learning for Climate.

This post is fixed term for 12 months

This project will advance the use of interpretable Machine Learning (ML) for climate science. The focus of this project is the El Nino-Southern Oscillation. This is the largest source of skill in seasonal forecasts with impacts on extreme weather worldwide. Current models are unable to predict the onset of a new El Nino event when forecasts are initialised in boreal spring. This prevents any action to mitigate the impacts of a forthcoming severe El Nino. However, recent breakthroughs have used ML to develop statistical algorithms that can skilfully predict El Nino many months in advance, improving on operational seasonal forecasting models. To understand the sources of predictability missing in seasonal forecast models, we must interpret and explain the behaviour of the ML algorithm. What features has the ML algorithm detected which have enabled it to make long-range forecasts?

The successful applicant is expected to work closely with Dr Christensen and external project partners to develop research strategies. The successful applicant will develop a ML model capable of long-range El Nino prediction, and interpret this model to understand how it produces such skilful forecasts. Finally, the successful applicant will assess operational forecasting models in the light of these results to quantify and understand their deficiencies. They will present the results at national and international meetings as well as publish them in high-impact publications.

Applicants should possess, or be very close to obtaining, a doctorate in physics, climate science, data science or a related field.

Applicants should demonstrate either an excellent understanding of the climate system with interest/experience in machine learning, or an excellent understanding of machine learning with interest/experience in climate science.

The post-holder will have the opportunity to teach.

Please direct enquiries about the role to Hannah Christensen hannah.christensen@physics.ox.ac.uk

Only applications received before midday 22 February 2021 can be considered. You will be required to upload a brief statement of research interests, CV and details of two referees as part of your online application.

Please find more details, including how to apply, by following the link below

https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=149203


*****

Best wishes,
Hannah Christensen
---
Hannah Christensen (She/Her)
Associate Professor of Physical Climate, Department of Physics, University of Oxford
David Richards Fellow, Wadham College, Oxford

email: hannah.christensen@physics.ox.ac.uk
tel: +44 (0)1865 701130

All information and attachments included in this e-mail are confidential to the intended recipient. If you have received it in error, please notify the sender and delete this message from your system. Any unauthorised use, disclosure, amendment, or copying is not permitted. This e-mail has been checked for viruses, but no liability is accepted for any damage caused by any virus transmitted with this e-mail. 

Your privacy and data protection are important to us. If you have any questions related to GDPR and data protection compliance, please visit https://www.wadham.ox.ac.uk/governance/wadham-college-gdpr-framework.






Go to: Periods · List Information · Index by: Date (or Reverse Date), Thread, Subject or Author.