Jobs

Jobs

Postdoctoral Research Assistant in Disruptive Innovative Computing for Weather and Climate

Applications are invited for a Postdoctoral Research Assistant in Disruptive Innovative Computing for Weather and Climate. The post is available initially for a fixed-term until 31 December 2020.

This project will explore the possibility of using one of two types of computing system for possible applications in weather/climate prediction: Field Programmable Gate Arrays (FPGAs), and Quantum Computers. A key novel ingredient relevant to either is the opportunity to explore imprecise computing, with either low-precision numerics on an FPGA, or using partially coherent qubits on a quantum computer. The successful candidate will work on co-development of hardware and software relevant for next generation weather/climate models, liaise with other scientists working on imprecise modelling and from other fields, present results at external meetings nationally and internationally, and publish results in peer reviewed journals. The postholder will have the opportunity to teach.

The successful candidate will have experience working with either FPGAs or quantum computers (in the latter case, theoretical experience will be sufficient). It is not necessary to have a background in weather or climate science, though some knowledge of nonlinear dynamics (e.g. of chaotic systems) would be helpful. Applicants should possess, or be very close to obtaining a doctorate in physics or a related field and ideally a strong background in computer science.

Please direct enquiries about the role to Professor Tim Palmer
(tim.palmer@physics.ox.ac.uk)

You will be required to upload a statement of research interests, CV and details of two referees as part of your online application.

Only applications received before 12.00 midday on Friday 5 January 2018 can be considered.

Please also look at the Job Description and Person Specification.

Please apply for this job via the University online recruitment system.

Application Deadline: 5 January 2018 by 12:00 midday

Postdoctoral Research Assistant in Climate Prediction

Applications are invited for a Postdoctoral Research Assistant in Climate Prediction. The post is available immediately for a fixed-term duration of 3 years.

The position is funded by the European Commission through the H2020 project "European Climate Prediction system (EUCP)". This project will develop an innovative ensemble climate prediction system based on high-resolution climate models for Europe for the near-term (~1-40 years) including improved methods to characterise uncertainty in climate prediction, regional downscaling, and evaluation against observations. The climate prediction system will be used to produce consistent, authoritative and actionable climate information which will form the basis for Europe-wide climate service activities to support climate-related risk assessments and climate change adaptation programmes.

The successful candidate will evaluate forecast quality globally and locally in terms of skill and reliability on interannual to decadal time scales, investigate the relative merits of different methods for the representation of model uncertainties, develop scientific methodologies to merge initialised predictions with non-initialised scenario projections on time scales of 1 - 40 years, combine climate forecast information on different scales from global models and regional simulations and establish the added value of regional models, and develop storylines and cases of plausible future weather consistent with the result of the combined forecasts across the full range of 1 - 40+ years by drawing on the initialised and non-initialised ensemble members. The postholder will have the opportunity to teach.

Applicants should possess, or be very close to obtaining a doctorate in climate science, mathematics, physics, statistical science or a related field. The candidate we are seeking should have a sound knowledge of climate science, climate modelling and climate prediction, or some experience with statistical verification of ensemble predictions and/or big data analysis from other areas of science. Candidates are expected to demonstrate excellent communication skills.

Please direct enquiries about the role to Dr Antje Weisheimer
(Antje.Weisheimer@Physics.ox.ac.uk)

Only applications received before midday (UK time) on Monday 15 January 2018 can be considered. You will be required to upload a supporting statement, CV and details of two referees as part of your online application.

Please also look at the Job Description and Person Specification.

Please apply for this job via the University online recruitment system.

Application Deadline: 15 January 2018 by 12:00 midday

Projects for Students

We welcome expressions of interest from potential DPhil (=PhD) candidates who would like to join our group. UK students can be funded through the University of Oxford's Doctoral Training Partnership in Environmental Research. There may be alternative funding opportunities for non-UK candidates.

We also welcome expressions of interest from undergraduate students who are interested in working in our group for their MPhys project or during the summer vacation.

MPhys project "Retrospective forecasts of winter and summer large-scale circulation changes during the 20th Century"

Forecasts of seasonal-mean anomalies of the climate using dynamical atmosphere-ocean circulation models based on the laws of physics are now routinely made at many operational meteorological forecast centres around the world. Such seasonal predictions provide estimates of seasonal-mean statistics of weather, typically up to four months ahead. In order to estimate how skilful seasonal forecasts are, the models are run in so-called retrospective forecast mode. This means that a period in the past, that can be verified with observations, is predicted using only information that would have been available at the time of the start of the forecasts. This project works with a long seasonal retrospective forecast data set that covers the entire 20th Century. The unusually long model forecast record allows the analysis of dominant modes of large-scale atmospheric variability, predictability and their changes on multi-decadal time scales. The student is going to analyse the existing ensemble forecast data set and compare it with a proxy data set of global observations.
Supervisors: Dr. Antje Weisheimer, Prof. Tim Palmer, Dr. Chris O’Reilly (all AOPP)
Contact: Dr. Antje Weisheimer