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"""We are currently inviting applications for a Postdoctoral Research Associate to work with Professor Justin Sirignano at the Mathematical Institute, University of Oxford. This is a three-year, fixed-term position, funded by a research grant from the Engineering and Physical Sciences Research Council (EPSRC). The starting date of this position is flexible with an earliest start date of 01 March 2024. We particularly welcome applications from individuals who are able to start between 01 March and 01 October 2024. The successful candidate will be part of a research group funded by a joint NSF and EPSRC grant to the University of Oxford and the Boston University: “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration between the postdoctoral research associate, Prof. Justin Sirignano (University of Oxford), and Prof. Konstantinos Spiliopoulos (Boston University). Neural network models in machine learning have achieved immense practical success over the past decade, revolutionizing fields such as image, text, and speech recognition. The training algorithms used for these complex machine learning problems – although successful in practice – are often ad hoc. Mathematical theory is yet to be established in many cases, and there is the potential to improve training algorithms and models via rigorous mathematical analysis. The objective of this research project is to develop new mathematical theory for the training algorithms and neural network models used in several key areas of machine learning. Analysis will leverage methods from stochastic analysis and weak convergence theory to study the asymptotics of online, stochastic training algorithms and neural network models as the number of hidden units becomes large. The research project is highly interdisciplinary, integrating methods from probability, partial differential equations, stochastic analysis, optimization, and machine learning. We proudly hold a departmental Athena SWAN Silver Award and an institutional Race Equality Charter Bronze Award, which guide our progress towards advancing racial and gender equality. As part of our strategic aim to improve staff equality and diversity, we would particularly welcome applications from women and BME candidates, who are currently under-represented in positions of this type within the department. Please direct informal enquiries to the Recruitment Coordinator at the email address given below, quoting vacancy reference 168225. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae including full list of publications, a statement of research interests, and the contact details of two referees as part of your online application. ( **NOTE: Applicants are responsible for contacting their referees and making sure that their letters are received by the closing date** ). Only applications received before 12.00 noon UK time on Monday 04 December, 2023 can be considered. """ . "IT homepage" . . . . "00000000"^^ . . "way/242287396" . "Address"@en . . _:N10c4137d17e447ed8d18750737e0837a "Radcliffe Observatory Quarter" . "HTML description of Postdoctoral Research Associate in Deep Learning" . "ha sede"@it . . "Notation3 description of Postdoctoral Research Associate in Deep Learning" . . . . . . . _:N4055325afacd4df380a568a4181a1141 "Woodstock Road" . . _:N4055325afacd4df380a568a4181a1141 "Oxford" . . . "homepage" . "depiction" . "preferred label"@en . "550" . 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We are currently inviting applications for a Postdoctoral Research Associate to work with Professor Justin Sirignano at the Mathematical Institute, University of Oxford. This is a three-year, fixed-term position, funded by a research grant from the Engineering and Physical Sciences Research Council (EPSRC). The starting date of this position is flexible with an earliest start date of 01 March 2024. We particularly welcome applications from individuals who are able to start between 01 March and 01 October 2024.

 

The successful candidate will be part of a research group funded by a joint NSF and EPSRC grant to the University of Oxford and the Boston University: “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration between the postdoctoral research associate, Prof. Justin Sirignano (University of Oxford), and Prof. Konstantinos Spiliopoulos (Boston University).

 

Neural network models in machine learning have achieved immense practical success over the past decade, revolutionizing fields such as image, text, and speech recognition. The training algorithms used for these complex machine learning problems – although successful in practice – are often ad hoc. Mathematical theory is yet to be established in many cases, and there is the potential to improve training algorithms and models via rigorous mathematical analysis.

 

The objective of this research project is to develop new mathematical theory for the training algorithms and neural network models used in several key areas of machine learning. Analysis will leverage methods from stochastic analysis and weak convergence theory to study the asymptotics of online, stochastic training algorithms and neural network models as the number of hidden units becomes large. The research project is highly interdisciplinary, integrating methods from probability, partial differential equations, stochastic analysis, optimization, and machine learning.

 

We proudly hold a departmental Athena SWAN Silver Award and an institutional Race Equality Charter Bronze Award, which guide our progress towards advancing racial and gender equality. As part of our strategic aim to improve staff equality and diversity, we would particularly welcome applications from women and BME candidates, who are currently under-represented in positions of this type within the department.

 

Please direct informal enquiries to the Recruitment Coordinator at the email address given below, quoting vacancy reference 168225.

 

Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae including full list of publications, a statement of research interests, and the contact details of two referees as part of your online application. (NOTE: Applicants are responsible for contacting their referees and making sure that their letters are received by the closing date).

 

Only applications received before 12.00 noon UK time on Monday 04 December, 2023 can be considered.
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"2023-12-04T12:00:00+00:00"^^ . . . . . "Voice"@en . . . "application/xhtml+xml" . "1865273547" . . . . . . . . . . "address"@en . . . "Title"@en . _:Nc06eb8faf01f45569341585da9142cba "OX1 2JD" . . . . "Recruitment Coordinator" . "4D06"^^ . . "name" . "telephone"@en . . . . """MATHEMATICAL INSTITUTE ANDREW WILES BUILDING Job Description and Selection Criteria Job title Postdoctoral Research Associate in Deep Learning Division Mathematical, Physical and Life Sciences Department Mathematical Institute Location Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG Grade and salary Grade 7: £38,205 - £44,263 per annum Hours Full time (37.5 hours per week) Contract type Fixed-term (36 months) Reporting to Professor Justin Sirignano Vacancy reference 168225 This is a full-time position that cannot be held concurrently with any other substantive post without the explicit permission of the Head of Department. This post is subject to a 12-month probationary period. Additional information This post is funded by Engineering and Physical Sciences Research Council (EPSRC). (PLEASE NOTE: Applicants are responsible for contacting their referees and making sure that their letters are received by the closing date). Research topic Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks Principal Investigator / Prof. Justin Sirignano and Prof. Konstantinos Spiliopoulos supervisor Project team Prof. Justin Sirignano and Prof. Konstantinos Spiliopoulos https://math.bu.edu/people/kspiliop/ Project web site Funding partner Recent publications Research group https://www.maths.ox.ac.uk/people/justin.sirignano This research project is funded by a joint NSF-EPSRC grant to Boston University and the University of Oxford. The postdoctoral position at the University of Oxford is funded by EPSRC. 1. “Mean field analysis of neural networks: A law of large numbers.” J. Sirignano and K. Spiliopoulos. SIAM Journal on Applied Mathematics, 80 (2), 725-752, 2020. 2. “Mean field analysis of neural networks: A central limit theorem.” J. Sirignano and K. Spiliopoulos, Stochastic Processes and their Applications, 130 (3), 1820-1852, 2020. 3. “Mean Field Analysis of Deep Neural Networks.” J. Sirignano and K. Spiliopoulos. Mathematics of Operations Research, 47 (1), 2021. 4. “Neural Q-learning for solving PDEs.” S. Cohen, D. Jiang, and J. Sirignano. Journal of Machine Learning Research, 24 (236), 1-49, 2023. 5. "Kernel Limit of Recurrent Neural Networks Trained on Ergodic Data Sequences.” S. Lam, J. Sirignano, and K. Spiliopoulos. ArXiv: 2308.14555, 2023. Data Science The role We invite applications for a Postdoctoral Research Associate to join the Mathematical Institute at the University of Oxford to conduct research on the mathematical theory of deep learning. This is a fixed-term position for up to 36 months. The successful candidate will join the Mathematical Institute and carry out research in an area related to the project. The starting date of this position is flexible with an earliest start date of 01 March 2024. We particularly welcome applications from individuals who are able to start between 01 March and 01 October 2024. This research project is funded by a joint NSF (Division of Mathematical Sciences) and EPSRC grant to the University of Oxford and the Boston University: “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with Prof. Justin Sirignano (University of Oxford), Prof. Konstantinos Spiliopoulos (Boston University), and PhD student researchers. Neural network models in machine learning have achieved immense practical success over the past decade, revolutionising fields such as image, text, and speech recognition. The training algorithms used for these complex machine learning problems -- although successful in practice -- are often ad hoc. Mathematical theory is yet to be established in many cases, and there is the potential to improve training algorithms and models via rigorous mathematical analysis. The primary purpose of this research project is to develop new mathematical theory for the training algorithms and neural network models used in several key areas of machine learning. Analysis will leverage methods from stochastic analysis and weak convergence theory to study 168225 PDRA_JobDescription_FINAL.docx 2 the asymptotics of online, stochastic training algorithms and neural network models as the number of hidden units becomes large. The research project is highly interdisciplinary, integrating methods from probability, partial differential equations, large deviations, stochastic analysis, optimization, and machine learning. Responsibilities The successful candidate will be expected to:  Manage their own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines  Adapt existing and develop new research methodologies and materials  Prepare working theories and analyse qualitative and/or quantitative data from a variety of sources, reviewing and refining theories as appropriate  Contribute ideas for new research projects  Develop ideas for generating research income, and present detailed research proposals to senior researchers  Collaborate in the preparation of research publications, and book chapters  Present papers at conferences or public meetings  Act as a source of information and advice to other members of the group on methodologies or procedures  Represent the research group at external meetings/seminars, either with other members of the group or alone  Carry out collaborative projects with colleagues in partner institutions, and research groups  Develop rigorous mathematical proofs for convergence analysis and asymptotic analysis of deep learning models, methods, and algorithms. It is the policy of the Mathematical Institute to give all PDRAs the opportunity to teach, where the conditions of the grant allow this, and to require teaching if there is a departmental need. Such teaching, if undertaken, will not exceed 3 hours per week for 24 weeks of the year and additional remuneration will be paid. It will normally be delivered as classes, but it might also involve giving lectures or college tutorials. 168225 PDRA_JobDescription_FINAL.docx 3 Selection criteria Your application will be judged only against the criteria which are set out below. You should ensure that your application shows clearly how your skills and experience meet these criteria. The Selection Committee for this process is expected to comprise: - Prof. Justin Sirignano (University of Oxford) Prof. Konstantinos Spiliopoulos (Boston University) To be confirmed The University is committed to fairness, consistency and transparency in selection decisions. Members of the selection committee are aware of the principles of equality of opportunity, fair selection and the risks of bias. If, for any reason, you have taken a career break, parental leave or have had an atypical career and wish to disclose this in your application, the selection committee will take this into account, recognising that the quantity of your experience may be reduced as a result. Essential selection criteria The successful candidate will be expected to meet the following criteria:  Have, or be close to completing, a PhD in mathematics or a related discipline.  Possess sufficient specialist knowledge in the discipline to work within established research programmes, including experience in the areas of: measure theory, real analysis, probability theory, and optimisation.  Ability to manage their own academic research.  Previous experience of contributing to publications/presentations.  Ability to contribute ideas for new research projects and research income generation.  Excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings. Pre-employment screening Standard checks If you are offered the post, the offer will be subject to standard pre-employment checks. You will be asked to provide: proof of your right-to-work in the UK; proof of your identity; and (if we haven’t done so already) we will contact the referees you have nominated. You will also be asked to complete a health declaration so that you can tell us about any health conditions or disabilities for which you may need us to make appropriate adjustments. Please read the candidate notes on the University’s pre-employment screening procedures at: https://www.jobs.ox.ac.uk/pre-employment-checks Proof of qualifications This post specifies that a PhD qualification is essential. If you are offered the post, you should therefore be in a position to provide proof of this qualification at least three months in advance of your proposed start date, and will be asked to provide the original PhD certificate or transcript as 168225 PDRA_JobDescription_FINAL.docx 4 part of the pre-employment checks. If you do not yet have either of these documents, you should provide an academic reference confirming submission of the thesis or that the qualification has been awarded. Failure to present either of these documents in a timely fashion could result in a delayed start, particularly where there is a need to apply for a valid work visa ahead of the appointment. 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. The Mathematical Institute The Mathematical Institute, as Oxford’s Department of Mathematics is known, is one of the leading mathematics departments in the world. Our mathematical research, impact and environment have twice been ranked first in the UK, in the 2021 and 2014 Research Excellence Framework exercises, a government review of research in all UK universities. The Mathematical Institute is the focus of research into both fundamental mathematics and its applications, and our inclusive nature and overall size are key factors in the provision of an outstanding research environment for our members. The large number of faculty, postdocs and students in the Mathematical Institute, all supported by excellent facilities, allows us to maintain a critical mass in research groups encompassing a wide spectrum of mathematics, while our integrated nature fosters collaboration between fields. We also host a large number of academic visitors. Our web pages (www.maths.ox.ac.uk) provide comprehensive information about all of our activities. The research activities of the Institute as a whole can be gauged from the web pages of the research groups and centres within the Institute (www.maths.ox.ac.uk/research). The range of our research interests is well reflected by the profile of our faculty as listed at www.maths.ox.ac.uk/people. Many members of the Institute have received prestigious prizes and other special recognition for their work; some recent examples can be found at www.maths.ox.ac.uk/news. The Mathematical Institute moved into the purpose-built Andrew Wiles Building in the University’s Radcliffe Observatory Quarter in September 2013. As well as providing offices for all staff and graduate students, it houses a range of other facilities available to members of the department, including the Whitehead Library, a large range of meeting rooms, teaching spaces, lecture 168225 PDRA_JobDescription_FINAL.docx 5 rooms, and social spaces, and a small laboratory for carrying out table-top experiments. For more information, see www.maths.ox.ac.uk/about-us . Teaching is central to the life of the Mathematical Institute and we have around 900 undergraduates on course, some on joint courses with other departments. We teach around 250 students each year across five taught master’s degree courses, and have over 250 doctoral students in residence at any one time. Our doctoral programme always attracts the best research students from across the world, and we have a broad mentoring and training programme. The Mathematical Institute strives to ensure that all staff and students are given the opportunities and support they need to achieve their potential. We are committed to equality of opportunities and to advancing women’s careers. We support staff returning from long-term absence with teaching relief, offer flexible working arrangements, and the department sponsors University nursery places to support the priority allocation of childcare to our staff. Further information about family support can be found below under University Benefits, Terms and Conditions. Our Equality, Diversity & Inclusion Committee1 contributes to many aspects of our work. As part of the department’s commitment to openness, inclusivity and transparency, we strongly encourage applications from all who consider they meet the requirements of the post, and particularly from women and ethnic minorities. We have a number of family-friendly policies, such as the right to apply for flexible working, hybrid working, and support for staff returning from periods of extended absence. We are committed to ensuring an inclusive interview process and will reimburse up to £250 towards any additional care costs (for a dependent child or adult) incurred as a result of attending an interview for this position, which may not be applicable if the interviews are held remotely. For more information on the Mathematical Institute, please visit: www.maths.ox.ac.uk The Mathematical Institute holds a silver Athena Swan award to recognise advancement of gender equality: representation, progression and success for all. The Mathematical, Physical and Life Sciences Division The Mathematical, Physical, and Life Sciences (MPLS) Division is one of the four academic divisions of the University. Oxford is widely recognised as one of the world's leading science universities and the MPLS Division is home to our non-medical sciences, with 10 academic departments that 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 tackles major societal and technological challenges – whether developing new energy solutions or improved cancer treatments, understanding climate change processes, or helping to preserve biodiversity, 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. The disciplines within the MPLS Division regularly appear at the highest levels in rankings, including the Times Higher Education and QS world rankings. Nationally, the quality of the Division’s research outputs and environment, and the resulting impact, was recognised through strong performances in the UK Research Excellence Framework in both 2014 and 2021. 1 The Mathematical Institute was a founding supporter of the London Mathematical Society’s Good Practice Scheme (www.lms.ac.uk/women/good-practice-scheme). We have held an Athena SWAN Silver Award since 2016. 168225 PDRA_JobDescription_FINAL.docx 6 MPLS is proud to be the home of some of the most creative and innovative scientific thinkers and leaders working in academe. Our senior researchers have been awarded some of the most significant scientific honours and we have a strong tradition of attracting and nurturing the very best early career researchers who regularly secure prestigious fellowships and faculty positions. MPLS continues in its work to support diversity in its staffing, seeing that it will bring benefits to all, and we are pleased to note that all academic departments in the Division hold Athena Swan Awards. We have around 7,300 full and part-time students (including approximately 3,400 graduate students) and play a major role in training the next generation of leading scientists. Oxford's international reputation for excellence in teaching is reflected in its position at the top of the major league tables and subject assessments. MPLS academics educate students of high academic merit and potential from all over the world. Through a mixture of lectures, practical work and the distinctive college tutorial system, students develop their ability to solve diverse mathematical, scientific and engineering problems. MPLS is dedicated to bringing the wonder and potential of science to the attention of audiences far beyond the world of academia. We have a strong commitment to supporting public engagement in science through initiatives including the Oxford Sparks portal (www.oxfordsparks.ox.ac.uk) and a large variety of outreach activities; these are crucial activities given so many societal and technological issues demand an understanding of the science that underpins them. We also bring the potential of our scientific efforts forward for practical and beneficial application to the real world and our desire, aided by the work of Oxford University Innovation and Oxford Sciences Innovation, is to link our best scientific minds with industry and public policy makers. For more information about the MPLS division, please visit: www.mpls.ox.ac.uk How to Apply Applications are made through our online recruitment portal. Information about how to apply is available on our Jobs website https://www.jobs.ox.ac.uk/how-to-apply. Your application will be judged solely on the basis of how you demonstrate that you meet the selection criteria stated in the job description. As part of your application you will be asked to provide details of two referees and indicate whether we can contact them now. You will also be required to upload a curriculum vitae, list of publications, statement of research interests 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). Please upload all documents as PDF files with your name and the document type in the filename, quoting vacancy reference 168225. Applicants should ask their referees to send their letters of reference DIRECTLY to The Recruitment Coordinator (Vacancies) Mathematical Institute, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG. Tel: 01865 273525: Email: vacancies@maths.ox.ac.uk by the closing date (a letter by email is sufficient) quoting the vacancy reference 168225. 168225 PDRA_JobDescription_FINAL.docx 7 Referees should preferably not, all be from the same institution and whenever possible one should be the applicant’s current, or most recent, supervisor. NOTE: reference letters must be received from your referees by the closing date for your application to be complete. All applications must be received by 12.00 noon UK time on Monday 04 December 2023. Interviews are anticipated to take place early January 2024. 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 department(s). 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). DATA PROTECTION: All data supplied by applicants will be used only for the purposes of determining their suitability for the post, and will be held in accordance with the principles of the Data Protection Act 1998 and the department’s data protection policy. https://www.maths.ox.ac.uk/members/policies/data-protection/statement Due to the large volume of recruitment that the department administers we are unable to provide feedback to non-shortlisted applicants. If you need help Application FAQs, including technical troubleshooting https://staff.web.ox.ac.uk/recruitment-support-faqs advice is available at: Non-technical questions about this job should be addressed to the recruiting department directly at vacancies@maths.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 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 168225 PDRA_JobDescription_FINAL.docx 8 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. 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. 168225 PDRA_JobDescription_FINAL.docx 9 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 dependents. 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. 168225 PDRA_JobDescription_FINAL.docx 10 """^^ . . . "2023-10-02T09:00:00+01:00"^^ . . "country name"@en . "false"^^ .