Senior Research Associate
2 weeks ago
Applications are invited for a Senior Research Associate in Statistical Machine Learning to join the research programme in Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) within the School of Mathematical Sciences at Lancaster University. This is an exciting opportunity to develop cutting-edge and theoretically-supported scalable statistical machine learning methods which are fit-for-purpose for modelling and analysing complex data structures, including, for example, large-scale spatio-temporal and network data.
This research is funded by a UKRI-EPSRC Turing AI Acceleration Fellowship, which is part of the UK government’s strategic investment in artificial intelligence research. This fellowship supports close collaborations with industrial stakeholders including, Microsoft Research, GCHQ, the Heilbronn Institute of Mathematical Research and the Alan Turing Institute. The PASCAL research programme is built on the foundation of probabilistic modelling and statistical learning to create a suite of algorithms, with theoretical guarantees on accuracy, that are capable of analysing large-scale data streams, learning deep latent data structures and providing trusted and interpretable decisions under uncertainty. As part of this research programme there is an opportunity to work closely with the project partners through research visits and secondments to gain real-world experience of industrial AI research.
You should have, or be close to completing, a PhD in Statistics, Machine Learning, or a related discipline. You will work directly with the principal investigator, Prof Christopher Nemeth, to undertake and support the research necessary to achieve the aims of the research grant. This will include, for example, publishing in leading statistics and machine learning journals/conference proceedings, presentation of research at workshops and conferences, developing code to implement new AI methods, and active involvement in project meetings. You will be experienced in one or more of the following areas: Bayesian statistics, computational statistics, statistical machine learning, probabilistic modelling. You will have demonstrated the ability to develop new statistical methodology and produce academic writing of the highest publishable quality is essential. Experience of developing research-level software is desirable but not essential.
This position is available from November 1st, and you will join us on an indefinite contract, however, the role remains contingent on external funding which, at this time, is for 8 months, however this may be extendable depending on additional funding.
Applications from people in all diversity groups are strongly encouraged.
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