Research Associate in Natural Language Processing and Machine Learning

Research Associate in Natural Language Processing and Machine Learning

Posted by Rebecca Martin on Mon, 17/10/2011 - 11:36

Faculty of Engineering

University of Sheffield - Department of Computer Science

Job Reference Number: UOS003396

Contract Type: Fixed-term for up to 36 months

Salary: Grade 7 £28,251 - £35,788 per annum.

Closing Date: 3 November 2011

Further details and online application: http://jobs.ac.uk/job/ADI311/

Summary:

The objective of this research project is to develop new machine learning techniques for predictive modelling of financial and political indices using text from social media sources (e.g., Twitter, Facebook and blogs). The project will develop algorithms for modelling the correlations between streaming social media data and the movement of various indices, viewed as a time-series. This problem presents unique challenges, both in terms of learning algorithms and in terms of efficient development for deployment in a real-time setting.

The appointee will work on the machine learning components of the project, namely developing new statistical models for our time-series data, and associated algorithms for training and prediction. The appointee will be responsible for developing new efficient algorithms for Bayesian inference. A central focus of the role will be developing fast online algorithms suitable for real-time application. The role will require strong programming skills, particularly for cluster and cloud computing infrastructure (e.g., MapReduce, Amazon EC2) or GPU computing (e.g., CUDA).

This is an opportunity to work in a well-connected international team with world-leading reputations in both the Natural Language Processing (NLP) and Machine Learning (ML) research groups at The University of Sheffield. The NLP group is well known internationally for its research, and is one of the largest research groups in computational linguistics and text engineering in the UK. The ML group is also very well respected, with expertise in fundamental machine learning and a range of application domains.

Candidates must have a PhD and a strong publication record in a relevant discipline. Solid knowledge of machine learning and natural language processing is required, as is excellent programming ability. The candidate should also have experience in one or more of the following areas: time-series modelling, dimensionality reduction/clustering, statistical machine translation, probabilistic graphical models, Markov Chain Monte Carlo and reinforcement learning.

This post is fixed-term for up to 36 months.