Applications are invited for a Postdoctoral Research Assistant in Clouds, Aerosols & Climate using Machine Learning.
The post is available for a fixed-term duration of 36 months
This position will be part of the ERC project Constraining the effects of aerosols on precipitation (RECAP and the EU H2020 collaborative project Constrained aerosol forcing for improved climate projections (FORCeS). With partners across Europe and worldwide, these projects are at the forefront of research into clouds and aerosols and their important role for climate.
The focus of this project will be to develop novel machine-learning based constraints on clouds, aerosols, their interactions and feedbacks, combining remote-sensing and in-situ observations, high-resolution and climate modelling with modern machine learning approaches.
This project will be conducted in close collaboration with machine learning experts in the Oxford Department of Computer Science, specifically with Prof. Yarin Gal’s Applied and Theoretical Machine Learning Group (https://oatml.cs.ox.ac.uk).
The successful candidate will work closely with national and international collaborators and are expected to develop innovative scientific approaches. The role will involve:
• Developing of novel machine-learning based constraints on clouds, aerosols, their interactions and feedbacks, combining remote-sensing and in-situ observations, high-resolution and climate modelling with modern machine learning approaches
• Developing innovative research and analysis strategies
• Carrying out collaborative projects with colleagues in partner institutions, and research groups
• Managing own academic research and administrative activities.
• Presenting results at national and international meetings
• Publishing results in high-impact peer-reviewed journals
• Contributing ideas for new research directions, and to the intellectual life of our dynamic research group, including meetings and collaborations
• Acting as a source of information and advice to other members of the group
There will be the possibility to co-advise a doctoral student and other teaching opportunities. This may include small group teaching and tutoring of undergraduates and graduate students.
Applicants should hold a doctorate/PhD (or be close to completion) in atmospheric/climate physics, machine learning or related fields, and have a strong background in either atmospheric physics or machine learning (with a demonstrable interest or experience in the other field).
Candidates are expected to demonstrate :
•Drive and ability to perform novel research of international standing
•The ability to work collaboratively, yet independently, as part of a team
•Strong computing skills, including the knowledge of UNIX/Linux, Fortran, Python, or other high-level languages
Please direct enquiries about the role to Professor Philip Stier firstname.lastname@example.org
Only applications received before midday (UK time) 15 February 2021 can be considered. You will be required to upload a supporting statement, CV and details of two referees as part of your online application.