Job description

Job summary

Applications are invited for several (5) Postdoctoral Research Associate positions in Network Data Science, Statistics and Probability to work on an EPSRC-funded programme on Network Stochastic Processes and Time Series (NeST).

NeST brings together the Universities of Bath, Bristol, Imperial College London, the London School of Economics and Political Science, Oxford, and York, with industrial and government partners BT, EDF, the GCHQ, the Office for National Statistics, Microsoft, Royal Mail, Securonix and FNA. Stochastic network data are of rapidly increasing ubiquity in many fields such as medicine, transportation, cybersecurity, the environment, finance, biology and economics, and NeST aims to achieve a step change in the modelling and prediction of evolving, inter-connected stochastic network processes.

Several posts are being advertised simultaneously across participating NeST universities; please visit https://www.ma.imperial.ac.uk/~gnason/nest.htmlto see a summary of current opportunities, and do apply to every project/institution which interests you. Projects that are available for this round of positions are:

• P2 Modelling and Forecasting Dynamic Networks via their Edges (Lead/Line Manager: Marina Knight. Location: York);
• P4 Dynamic Graph Embeddings: Procedures and Inference (Lead/Line Manager: Nick Heard. Location: Imperial, London);
• P5 Network Count Processes (Lead/Line Manager: Ed Cohen. Location: Imperial, London);
• P6 Novel Long-Memory Spectral Domain Modelling for Network Data (Lead/Line Manager: Matt Nunes. Location: Bath);
• P7 Network Time Series in Continuous Time: Modelling and Estimation (Lead/Line Manager: Almut Veraart. Location: Imperial, London).
• P8 Novel Network Time Series Models with Application to Advanced Modelling and Prediction of Government Flow Data Sets (Lead/Line Manager: Matt Nunes. Location: Bath).

Duties and responsibilities

As part of the NeST team, you will contribute to realising a substantial coordinated push to create, develop and apply innovative new models, computational techniques, and underpinning theory, in response to real applied problems spurred by dynamic networks in many contexts.

Initially, you will be attached to one or two research projects led by one or two investigators (and line managed by one of them). As part of the NeST team, you will have access to, and potential opportunities to work with a larger team consisting of academics (Ed Cohen, Nick Heard, Marina Knight, Guy Nason, Matt Nunes, Gesine Reinert, Patrick Rubin-Delanchy, Almut Veraart and Qiwei Yao) collectively covering a wide range of research in NeST areas, and a growing cohort of postdoctoral and PhD student colleagues spread over the constituent universities.  (See Job Description for further details and role responsibilities and requirements.)

Essential requirements

• Hold a PhD (or equivalent) in Statistics, Applied Probability, or a closely related area


• Advanced knowledge in advanced statistical methodologies amongst which (dynamic) network analysis, point processes, stochastic processes associated with networks/graphs, changepoint analysis, discrete/continuous-time series, Fourier/wavelet analysis, spatio-temporal modelling, their strengths and limitations.


• Excellent written communication skills and the ability to write clearly and succinctly for publication.


• Ability to conduct a detailed review of recent literature.


• Ability to develop and apply new concepts.


• Creative approach to problem-solving.


• Ability to organise own work with minimal supervision.


• Ability to prioritise own work in response to deadlines.


• Practical experience within a research environment and / or publication in relevant and refereed journals.


• Ability to work as part of a team and to work independently using own initiative.


• Willingness to work as part of a team and to be open-minded and cooperative

Further information

*Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £38,194 - £41,388 per annum.

The position is full-time, initially for 24 months. The successful candidates are expected to take up the position as soon as possible, but ideally before October 2023 for the right candidates. The salary is on the Imperial College pay scale for a Research Associate position. All applicants with a relevant background and research skills are eligible to apply.

Please apply by completing the online application associated to this advert.

Provisional interview timeline: candidates will be invited to a face-to-face interview at their chosen institution(s) in the interval 29^thMay to 9^thJune 2023. Offers will be made only after all interviews have taken place.

Should you require any further details on the role please contact: Ed Cohen (e.cohen@imperial.ac.uk), Nick Heard (n.heard@imperial.ac.uk) or Almut Veraart (a.veraart@imperial.ac.uk). 

The College is currently trialling a Work Location Framework until early 2023.  Hybrid working may be considered for this role and the role holder may be expected to work 60% or more of their time onsite, with 40% the minimum time spent onsite.  The opportunity for hybrid working will be discussed at interview.

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

The College believes that the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level.

http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research/ 

Documents

• Job Description - NAT01413.pdf