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Postdoctoral Research Assistant in Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data
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Description of Postdoctoral Research Assistant in Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data
Atmospheric, Oceanic and Planetary Physics
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_________________________________________________________________________ Job Description and Selection Criteria Job title Postdoctoral Researcher in Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data with Focus on Clouds Division Mathematical Physical and Life Sciences Department Physics Location Atmospheric Physics, Parks Road, Oxford, OX1 3PU Grade and salary Grade 7: £36,024 - £44,263 per annum Hours Full time Contract type Fixed-term for 36 months Reporting to Prof. Philip Stier Vacancy reference 168670 Additional information Closing date – midday on 15 January 2023 Research topic Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data with Focus on Clouds Principal Investigator / supervisor Prof. Philip Stier Project team Climate Processes Group Project web site https://www.physics.ox.ac.uk/research/group/climate-processes Funding partner The funds supporting these positions research project are provided by the UKRI underwrite for the EU Horizon Europe project Earth Observation & Weather Data Federation with AI Embeddings (Embed2Scale). Recent publications https://www.physics.ox.ac.uk/research/group/climateprocesses/publications The role We are looking for a post-doctoral researcher to join the dynamic Climate Processes Group (http://www.physics.ox.ac.uk/research/climate-processes) within the sub-Department of Atmospheric, Oceanic and Planetary Physics (AOPP). The post is available for a fixed-term period of 36 months. This position, part of EU Horizon Europe project Earth Observation & Weather Data Federation with AI Embeddings (Embed2Scale) with partners across Europe, will tackle our understanding and compressed representations of clouds in the climate system through the development and utilisation of geospatial foundation models. Successful applicants will work closely with IBM and our international collaborators and are expected to develop innovative research approaches. The role also includes the possibility to coadvise doctoral students. Results should be presented at national and international meetings as well as published in leading subject and high-impact publications. Responsibilities          Development of novel approaches to for the analysis and compression of atmospheric data through the training of deep neural networks with self-supervised learning to transform raw geo-information into embeddings with up to 1000-fold compression. The development of original research and analysis strategies. Carry out collaborative research with colleagues at IBM and other partner institutions, and research groups. Manage own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. The presentation of results at national and international meetings. Publication of results in leading subject and high-impact peer-reviewed journals. Contribute ideas for new research projects. Contribution to the intellectual life of a dynamic research group, including meetings and collaborations. The possibility to co-advise doctoral students and other teaching opportunities. This may include small group teaching and tutoring of undergraduates and graduate students. Selection criteria Essential       Doctorate / PhD (or be close to obtaining) in atmospheric physics, physics, data sciences, AI, machine learning or related fields First degree Strong background and track record in atmospheric physics / physics or data sciences (AI, ML) with a strong interest to expand into the complementary subject area. Demonstrated 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 168670 Embed2Scale PDRA JD 2023.docx 2 Desirable    Strong quantitative / theoretical skills with an interest in practical applications. Understanding of the foundations of machine learning, deep neural networks and selfsupervised learning Experience with the use of machine learning workflows About the University of Oxford Welcome to the University of Oxford. We aim to lead the world in research and education for the benefit of society both in the UK and globally. Oxford’s researchers engage with academic, commercial and cultural partners across the world to stimulate high-quality research and enable innovation through a broad range of social, policy and economic impacts. We believe our strengths lie both in empowering individuals and teams to address fundamental questions of global significance, while providing all our staff with a welcoming and inclusive workplace that enables everyone to develop and do their best work. Recognising that diversity is our strength, vital for innovation and creativity, we aspire to build a truly diverse community which values and respects every individual’s unique contribution. While we have long traditions of scholarship, we are also forward-looking, creative and cuttingedge. Oxford is one of Europe's most entrepreneurial universities and we rank first in the UK for university spin-outs, and in recent years we have spun out 15-20 new companies every year. We are also recognised as leaders in support for social enterprise. Join us and you will find a unique, democratic and international community, a great range of staff benefits and access to a vibrant array of cultural activities in the beautiful city of Oxford. For more information, please visit www.ox.ac.uk/about/organisation. Department of Physics Oxford Physics is one of the largest and most eminent departments in Europe – pursuing forefront research alongside training the next generation of leaders in Physics. With an academic staff of over one hundred our activities range from fundamental particles to the furthest reaches of the universe to manipulating matter on an atomic scale. Oxford physicists are probing new ways to harness solar energy, modelling the Earth's atmosphere to predict the future climate, exploring computation on the quantum scale and executing calculations that reveal the fundamental structure of space and time. For more information please visit: http://www2.physics.ox.ac.uk/ Atmospheric, Oceanic and Planetary Physics Sub-department The post-holder will be based in the Atmospheric, Oceanic and Planetary Physics subdepartment, which is one of the six sub-departments that together make up the Department of Physics; these are Astrophysics, Atomic and Laser Physics, Atmospheric, Oceanic and Planetary Physics, Condensed Matter Physics, Particle Physics and Theoretical Physics, with a seventh function (Central Physics) providing administrative and technical support to these subdepartments. AOPP’s research can be broadly categorized into climate physics and planetary 168670 Embed2Scale PDRA JD 2023.docx 3 research. Cross-cutting themes of planetary circulation & composition as well as cross-cutting methodologies (theory, observations, modelling, AI/ML) intrinsically link these research areas. Members of all sub-departments take part in research, teaching and matters such as examinations, discussion of syllabi, lectures and liaison with undergraduates and postgraduate students. For more information please visit: https://www.physics.ox.ac.uk/research/subdepartment/atmospheric-oceanic-and-planetaryphysics Mathematical, Physical & Life Sciences Division The Mathematical, Physical and Life Sciences (MPLS) Division is one of the four academic divisions of the University of Oxford. The MPLS Division's 10 departments and 3 interdisciplinary units span the full spectrum of the mathematical, computational, physical, engineering and life sciences, and undertake both fundamental research and cutting-edge applied work. Our research addresses major societal and technological challenges and is increasingly focused on key interdisciplinary issues. We collaborate closely with colleagues in Oxford across the medical sciences, social sciences and humanities, and with other universities, research organisations and industrial partners across the globe in pursuit of innovative research geared to address critical and fundamental scientific questions. For more information please visit: http://www.mpls.ox.ac.uk/ Athena Swan Charter The Department of Physics holds a silver Athena Swan award to recognise advancement of gender equality: representation, progression and success for all. How to apply If you would like to apply, click on the Apply Now button on the ‘Job Details’ page and follow the on-screen instructions to register as a new user or log-in if you have applied previously. Please provide details of two referees and indicate whether we can contact them now. Referees are also required to submit their references to lucy.li@physics.ox.ac.uk by the closing date. You will also be asked to upload a CV and a supporting statement. The supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. This may include experience gained in employment, education, or during career breaks (such as time out to care for dependants). Your application will be judged solely on the basis of how you demonstrate that you meet the selection criteria stated in the job description. Please upload all documents as PDF files with your name and the document type in the filename. All applications must be received by midday on the closing date stated in the online advertisement. 168670 Embed2Scale PDRA JD 2023.docx 4 Information for priority candidates A priority candidate is a University employee who is seeking redeployment because they have been advised that they are at risk of redundancy, or on grounds of ill-health/disability. Priority candidates are issued with a redeployment letter by their employing departments. If you are a priority candidate, please ensure that you attach your redeployment letter to your application (or email it to the contact address on the advert if the application form used for the vacancy does not allow attachments) If you need help Application FAQs, including technical troubleshooting advice is available at: https://staff.web.ox.ac.uk/recruitment-support-faqs Non-technical questions about this job should be addressed to the recruiting department directly recruitment@physics.ox.ac.uk To return to the online application at any stage, please go to: www.recruit.ox.ac.uk. Please note that you will receive an automated email from our online recruitment portal to confirm receipt of your application. Please check your spam/junk mail if you do not receive this email. Important information for candidates Pre-employment screening Please note that the appointment of the successful candidate will be subject to standard preemployment screening, as applicable to the post. This will include right-to-work, proof of identity and references. We advise all applicants to read the candidate notes on the University’s preemployment screening procedures, found at: www.ox.ac.uk/about/jobs/preemploymentscreening/. Important information for candidates Data Privacy Please note that any personal data submitted to the University as part of the job application process will be processed in accordance with the GDPR and related UK data protection legislation. For further information, please see the University’s Privacy Notice for Job Applicants at: https://compliance.admin.ox.ac.uk/job-applicant-privacy-policy. The University’s Policy on Data Protection is available at: https://compliance.admin.ox.ac.uk/data-protection-policy. The University’s policy on retirement The University operates an Employer Justified Retirement Age (EJRA) for very senior research posts at grade RSIV/D35 and clinical equivalents E62 and E82, which with effect from 1 October 2023 will be 30 September before the 70th birthday. The justification for this is explained at: https://hr.admin.ox.ac.uk/the-ejra. For existing employees on these grades, any employment beyond the retirement age is subject to approval through the procedures: https://hr.admin.ox.ac.uk/the-ejra. There is no normal or fixed age at which staff in posts at other grades have to retire. Staff at these grades may elect to retire in accordance with the rules of the applicable pension scheme, as may be amended from time to time. 168670 Embed2Scale PDRA JD 2023.docx 5 Equality of opportunity Entry into employment with the University and progression within employment will be determined only by personal merit and the application of criteria which are related to the duties of each particular post and the relevant salary structure. In all cases, ability to perform the job will be the primary consideration. No applicant or member of staff shall be discriminated against because of age, disability, gender reassignment, marriage or civil partnership, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. 168670 Embed2Scale PDRA JD 2023.docx 6 Benefits of working at the University Employee benefits University employees enjoy 38 days’ paid holiday, generous pension schemes, travel discounts, and a variety of professional development opportunities. Our range of other employee benefits and discounts also includes free entry to the Botanic Gardens and University colleges, and discounts at University museums. See https://hr.admin.ox.ac.uk/staff-benefits University Club and sports facilities Membership of the University Club is free for all University staff. The University Club offers social, sporting, and hospitality facilities. Staff can also use the University Sports Centre on Iffley Road at discounted rates, including a fitness centre, powerlifting room, and swimming pool. See www.club.ox.ac.uk and https://www.sport.ox.ac.uk/. Information for staff new to Oxford If you are relocating to Oxfordshire from overseas or elsewhere in the UK, the University's Welcome Service website includes practical information about settling in the area, including advice on relocation, accommodation, and local schools. See https://welcome.ox.ac.uk/ There is also a visa loan scheme to cover the costs of UK visa applications for staff and their dependants. See https://staffimmigration.admin.ox.ac.uk/visa-loan-scheme Family-friendly benefits With one of the most generous family leave schemes in the Higher Education sector, and a range of flexible working options, Oxford aims to be a family-friendly employer. We also subscribe to the Work+Family Space, a service that provides practical advice and support for employees who have caring responsibilities. The service offers a free telephone advice line, and the ability to book emergency back-up care for children, adult dependents and elderly relatives. See https://hr.admin.ox.ac.uk/my-family-care Childcare The University has excellent childcare services, including five University nurseries as well as University-supported places at many other private nurseries. For full details, including how to apply and the costs, see https://childcare.admin.ox.ac.uk/ Disabled staff We are committed to supporting members of staff with disabilities or long-term health conditions. For further details, including information about how to make contact, in confidence, with the University’s Staff Disability Advisor, see https://edu.admin.ox.ac.uk/disability-support Staff networks The University has a number of staff networks including the Oxford Research Staff Society, BME staff network, LGBT+ staff network and a disabled staff network. You can find more information at https://edu.admin.ox.ac.uk/networks The University of Oxford Newcomers' Club The University of Oxford Newcomers' Club is an organisation run by volunteers that aims to assist the partners of new staff settle into Oxford, and provides them with an opportunity to meet people and make connections in the local area. See www.newcomers.ox.ac.uk. 168670 Embed2Scale PDRA JD 2023.docx 7
Applications are invited for a Postdoctoral Researcher in Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data with Focus on Clouds The post is available for a fixed-term duration of 36 months. This position, part of EU Horizon Europe project Earth Observation & Weather Data Federation with AI Embeddings (Embed2Scale), with IBM and partners across Europe, will be at the forefront of tackling our understanding and compressed representations of clouds in the climate system through the development and utilisation of geospatial foundation models. The successful candidate will work closely with IBM and our international collaborators and be expected to develop innovative approaches. The results should be presented at national and international meetings as well as published in leading subject and high-impact publications. Applicants should possess, or be close to obtaining of a PhD / Doctorate in in atmospheric physics, physics, data sciences, AI, machine learning or related fields. The ability and drive to work collaboratively, yet independently, as part of a team is essential. Candidates are expected to demonstrate an excellent understanding of atmospheric physics / physics or data sciences (AI, ML), with a strong interest to expand into the complementary subject area, and a demonstrated drive and ability to perform novel research of international standing. The postholder will have the opportunity to teach. Please direct enquiries about the role to Prof. Philip Stier (philip.stier@physics.ox.ac.uk) Only applications received before midday 15 January 2024 can be considered. You will be required to upload a supporting statement, CV and details of two referees as part of your online application. Referees are also required to submit their references to lucy.li@physics.ox.ac.uk by the closing date.
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Applications are invited for a Postdoctoral Researcher in Geospatial Foundation Models for the Analysis and Compression of Atmospheric Data with Focus on Clouds The post is available for a fixed-term duration of 36 months. This position, part of EU Horizon Europe project _Earth Observation & Weather Data Federation with AI Embeddings (Embed2Scale),_ with IBM and partners across Europe, will be at the forefront of tackling our understanding and compressed representations of clouds in the climate system through the development and utilisation of geospatial foundation models. The successful candidate will work closely with IBM and our international collaborators and be expected to develop innovative approaches. The results should be presented at national and international meetings as well as published in leading subject and high-impact publications. Applicants should possess, or be close to obtaining of a PhD / Doctorate in in atmospheric physics, physics, data sciences, AI, machine learning or related fields. The ability and drive to work collaboratively, yet independently, as part of a team is essential. Candidates are expected to demonstrate an excellent understanding of atmospheric physics / physics or data sciences (AI, ML), with a strong interest to expand into the complementary subject area, and a demonstrated drive and ability to perform novel research of international standing. The postholder will have the opportunity to teach. Please direct enquiries about the role to Prof. Philip Stier (philip.stier@physics.ox.ac.uk) Only applications received before midday 15 January 2024 can be considered. You will be required to upload a supporting statement, CV and details of two referees as part of your online application. Referees are also required to submit their references to lucy.li@physics.ox.ac.uk by the closing date.
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Department of Physics, Clarendon Laboratory, Parks Road, Oxford
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