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Machine Learning
PhD: Development and application of Machine Learning for downscaling climate predictions/projections Contract
Job Description:
About the project/work tasks
Do research into methods for improving the quality and transferability of emulators across different global climate models and time scales (i.e. prediction – projections).
Test the transferability of an existing state-of-the-art machine learning emulator (developed for use over Europe) to tropical conditions.
Disseminate research findings to the research community and the broader public through activities organized by the project.
Collaborating with other project members performing research on climate predictions and km-scale regional downscaling.
Applicants must hold a master’s degree or equivalent education in atmospheric sciences, statistics, computer science, applied mathematics or related disciplines. Master students can apply provided they complete their final master exam by 15.08.2024. It is a condition of employment that the master’s degree has been awarded.
Experience in atmospheric physics, climate dynamics, and statistical modelling is required.
Experience with scientific programming using Python, R, Matlab, Julia, C/C++ or similar is required.
Experience with machine learning is an advantage.
Experience with climate models (regional or global) and climate data analysis is an advantage.
Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills.
Applicants must have good English skills, written and oral.
Personal and relational qualities will be emphasized. Ambitions and potential will also count when evaluating the candidates.
a good and professionally stimulating working environment.
salary as PhD research fellow (code 1017) in the state salary scale. This constitutes a gross annual salary of NOK 532 200 (equivalent to pay grade 54). Further increases in salary are made according to length of service in the position.
enrolment in the Norwegian Public Service Pension Fund
good welfare benefits
a brief account of the applicant’s research interests and motivation for applying for the position
the names and contact information for two referees. One of these should be the main advisor for the master’s thesis or equivalent thesis
CV
transcripts and diplomas showing completion of the bachelor’s and master’s degrees. If you have not yet completed your master’s degree, please submit a statement from your institution confirming the expected date of award of your master’s degree. Your master’s degree must be documented with transcripts and/or diploma, alternatively a confirmation from your institution on completed degree and your grade, by 15.08.2024.
relevant certificates/references
approved documentation of proficiency in English (if required, cf. English language requirements for PhD admission)
a list of any works of a scientific nature (publication list)
any publications in your name
Despite significant improvements in climate modelling during the last decades, predictions of climate from monthly to decadal time scales remain challenging. Further, both predictions and projections of climate and its impacts are highly uncertain at the scales which are most relevant for impacts and adaptation. I4C aims to improve the quality, accessibility and usability of near-term climate information and services at local to regional scales to strengthen and support end-user adaptation and planning. While CORDEX aims to develop robust assessments, and physical understanding, of climate change impacts at local to regional scales.
This position will focus on the application of machine learning to develop computationally efficient emulators of high-resolution state-of-the art regional climate models. The goal of the emulators is to learn the relationship between large-scale predictions made by coarse resolution dynamical models and local surface variables of interest (such as near surface temperature, precipitation, wind speeds). This is accomplished by training the emulators to mimic the results of computationally expensive high-resolution regional climate models. The result is a suite of computationally lightweight algorithms capable of generating large ensembles that can translate coarse resolution operational climate predictions or projections into robust and reliable regional and local information.
Work tasks
The work will be done in close collaboration with scientific partners in I4C, in particularly MeteoFrance and NORCE (Norwegian research centre). The University of Bergen is part of the Norwegian Artificial Intelligence Research Consortium and the candidate will work in a highly international working environment as part of the Bjerknes Center, whichc is co-located with the Geophysical Institute, University of Bergen. BCCR is the largest climate research centre in the Nordic countries and among the leading centres in Europe with around 200 scientists from 37 countries.
Qualifications and personal qualities
Special requirements for the position
The University of Bergen is subjected to the regulation for export control system. The regulation will be applied in the processing of the applications.
About the PhD position
The fellowship will be for a period of 3 years, with the possibility for a 4th year, consisting of 25 % compulsory work (e.g. teaching responsibilities at the department) distributed over the employment period. The 4th year is contingent on the qualifications of the candidate and the teaching needs of the department and will be decided by the head of department upon appointment.
The employment period may be reduced if you have previously been employed in a qualifying post (e.g. research fellow, research assistant).
About the research training
As a PhD Research Fellow, you must participate in an approved educational programme for a PhD degree within a period of 3 years. The deadline for applying for admission to the PhD programme at The Faculty of Mathematics and Natural Sciences is 2 months after you start your position or after the start of the research project that will lead to the PhD degree. It is a condition that you satisfy the enrollment requirements for the PhD programme at the University of Bergen.
We can offer
Your application must include
The application and appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge.
General information
For further details about the position, please contact Stefan Sobolowski (Research Leader/Assoc. Professor, The Bjerknes Center for Climate Research & the Geophysical Institute, University of Bergen; stefans@uib.no)
For HR related questions contact Maria Svåsand (Maria.Svasand@uib.no)
The state labour force shall reflect the diversity of Norwegian society to the greatest extent possible. Age and gender balance among employees is therefore a goal. It is also a goal to recruit people with immigrant backgrounds. People with immigrant backgrounds and people with disabilities are encouraged to apply for the position.
The University of Bergen applies the principle of public access to information when recruiting staff for academic positions.
Information about applicants may be made public even if the applicant has asked not to be named on the list of persons who have applied. The applicant must be notified if the request to be omitted is not met.
The successful applicant must comply with the guidelines that apply to the position at all times.
For further information about the recruitment process, click here.
via Unsplash
Life as a PhD candidate at UiB
Marion Claireaux tells about life and work as a PhD candidate at UiB.
…
(Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video)
via Unsplash
About UiB
The University of Bergen is a renowned educational and research institution, organised into seven faculties and approximately 54 institutes and academic centres. Campus is located in the centre of Bergen with university areas at Nygårdshøyden, Haukeland, Marineholmen, Møllendalsveien and Årstad.
About the project/work tasks
Do research into methods for improving the quality and transferability of emulators across different global climate models and time scales (i.e. prediction – projections).
Test the transferability of an existing state-of-the-art machine learning emulator (developed for use over Europe) to tropical conditions.
Disseminate research findings to the research community and the broader public through activities organized by the project.
Collaborating with other project members performing research on climate predictions and km-scale regional downscaling.
Applicants must hold a master’s degree or equivalent education in atmospheric sciences, statistics, computer science, applied mathematics or related disciplines. Master students can apply provided they complete their final master exam by 15.08.2024. It is a condition of employment that the master’s degree has been awarded.
Experience in atmospheric physics, climate dynamics, and statistical modelling is required.
Experience with scientific programming using Python, R, Matlab, Julia, C/C++ or similar is required.
Experience with machine learning is an advantage.
Experience with climate models (regional or global) and climate data analysis is an advantage.
Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills.
Applicants must have good English skills, written and oral.
Personal and relational qualities will be emphasized. Ambitions and potential will also count when evaluating the candidates.
a good and professionally stimulating working environment.
salary as PhD research fellow (code 1017) in the state salary scale. This constitutes a gross annual salary of NOK 532 200 (equivalent to pay grade 54). Further increases in salary are made according to length of service in the position.
enrolment in the Norwegian Public Service Pension Fund
good welfare benefits
a brief account of the applicant’s research interests and motivation for applying for the position
the names and contact information for two referees. One of these should be the main advisor for the master’s thesis or equivalent thesis
CV
transcripts and diplomas showing completion of the bachelor’s and master’s degrees. If you have not yet completed your master’s degree, please submit a statement from your institution confirming the expected date of award of your master’s degree. Your master’s degree must be documented with transcripts and/or diploma, alternatively a confirmation from your institution on completed degree and your grade, by 15.08.2024.
relevant certificates/references
approved documentation of proficiency in English (if required, cf. English language requirements for PhD admission)
a list of any works of a scientific nature (publication list)
any publications in your name
Despite significant improvements in climate modelling during the last decades, predictions of climate from monthly to decadal time scales remain challenging. Further, both predictions and projections of climate and its impacts are highly uncertain at the scales which are most relevant for impacts and adaptation. I4C aims to improve the quality, accessibility and usability of near-term climate information and services at local to regional scales to strengthen and support end-user adaptation and planning. While CORDEX aims to develop robust assessments, and physical understanding, of climate change impacts at local to regional scales.
This position will focus on the application of machine learning to develop computationally efficient emulators of high-resolution state-of-the art regional climate models. The goal of the emulators is to learn the relationship between large-scale predictions made by coarse resolution dynamical models and local surface variables of interest (such as near surface temperature, precipitation, wind speeds). This is accomplished by training the emulators to mimic the results of computationally expensive high-resolution regional climate models. The result is a suite of computationally lightweight algorithms capable of generating large ensembles that can translate coarse resolution operational climate predictions or projections into robust and reliable regional and local information.
Work tasks
The work will be done in close collaboration with scientific partners in I4C, in particularly MeteoFrance and NORCE (Norwegian research centre). The University of Bergen is part of the Norwegian Artificial Intelligence Research Consortium and the candidate will work in a highly international working environment as part of the Bjerknes Center, whichc is co-located with the Geophysical Institute, University of Bergen. BCCR is the largest climate research centre in the Nordic countries and among the leading centres in Europe with around 200 scientists from 37 countries.
Qualifications and personal qualities
Special requirements for the position
The University of Bergen is subjected to the regulation for export control system. The regulation will be applied in the processing of the applications.
About the PhD position
The fellowship will be for a period of 3 years, with the possibility for a 4th year, consisting of 25 % compulsory work (e.g. teaching responsibilities at the department) distributed over the employment period. The 4th year is contingent on the qualifications of the candidate and the teaching needs of the department and will be decided by the head of department upon appointment.
The employment period may be reduced if you have previously been employed in a qualifying post (e.g. research fellow, research assistant).
About the research training
As a PhD Research Fellow, you must participate in an approved educational programme for a PhD degree within a period of 3 years. The deadline for applying for admission to the PhD programme at The Faculty of Mathematics and Natural Sciences is 2 months after you start your position or after the start of the research project that will lead to the PhD degree. It is a condition that you satisfy the enrollment requirements for the PhD programme at the University of Bergen.
We can offer
Your application must include
The application and appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge.
General information
For further details about the position, please contact Stefan Sobolowski (Research Leader/Assoc. Professor, The Bjerknes Center for Climate Research & the Geophysical Institute, University of Bergen; stefans@uib.no)
For HR related questions contact Maria Svåsand (Maria.Svasand@uib.no)
The state labour force shall reflect the diversity of Norwegian society to the greatest extent possible. Age and gender balance among employees is therefore a goal. It is also a goal to recruit people with immigrant backgrounds. People with immigrant backgrounds and people with disabilities are encouraged to apply for the position.
The University of Bergen applies the principle of public access to information when recruiting staff for academic positions.
Information about applicants may be made public even if the applicant has asked not to be named on the list of persons who have applied. The applicant must be notified if the request to be omitted is not met.
The successful applicant must comply with the guidelines that apply to the position at all times.
For further information about the recruitment process, click here.
via Unsplash
Life as a PhD candidate at UiB
Marion Claireaux tells about life and work as a PhD candidate at UiB.
…
(Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video)
via Unsplash
About UiB
The University of Bergen is a renowned educational and research institution, organised into seven faculties and approximately 54 institutes and academic centres. Campus is located in the centre of Bergen with university areas at Nygårdshøyden, Haukeland, Marineholmen, Møllendalsveien and Årstad.
