We seek outstanding candidates to work on a two-year project to develop machine learning tools to predict NHS hospital attendance at St Mary’s Hospital in Paddington, London.
We seek an exceptional and technically proficient Research Assistant or Associate to utilise the current state-of-the-art techniques from machine learning and statistics to investigate:
- To what degree is demand fundamentally predictable from an information theoretic and entropy view.
- What statistical architecture best captures the underlying temporal process (e.g ARIMAs, Smoothing, Long short term memory/GRU recurrent nets, seq2seq architectures etc.)
- What quantitative accuracy can be expected from forecasts and how this degrades with the level of aggregation and time horizon of the forecast,
- By collecting accessory data, and including random effects, what are the underlying factors contributing to changes in demand,
- Can detailed calendar information such as school holidays, weather, and public events be used to leverage predictive accuracy
- Is it possible to characterise the frequency and occurrence of anomalous surges of excessive demand
- Can our framework be applied efficiently in near-real time settings?
Candidates with a machine learning background and experience with time series are encouraged to apply.
For informal inquiries contact Samir Bhatt (Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo.) or
Seth Flaxman (Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo.)
https://www.jobs.ac.uk/job/BMY128/research-assistant-orassociate
