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Text Analytics with Deep Learning for Predicting Dynamic Economic Systems

About This PhD Project

Project Description

- Basic Funding details – Full time Home/EU fees and a stipend of £15,009 p.a.
- Application deadline: Tuesday 7th May 2019
- Start date: October 2019
- Duration: 3 years full-time
- Location: Colchester campus
- Lead department: CSEE
- Connected department: Mathematical Sciences

A brief overview of the studentship

Goal of the PhD studentship is to develop novel text analytics methods based on modern Deep Neural Networks. The novel text analytics methods will be applied to better understand and analyse the complexity of real-world activities and dynamics in socio-economic systems such as stock markets, health care services, innovation processes and others. Human language technology, and text analytics in particular, has been a key area of research for more than 40 years at Essex University. The PhD studentship will greatly benefit from this embedding and the strong interdisciplinary research between computer science and mathematical sciences.

Detailed funding information – The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see for further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Primary supervisor – Ansgar Scherp is professor for Natural Language Processing and Data Analytics. He has an excellent research reputation in text analytics, especially for text classification, and graph data mining. He develops approaches for data analyses by combining symbolic and statistical methods that bring together the fields of Information Retrieval, Machine Learning and Artificial Intelligence.

Co-supervisor – Dr Spyridon Vrontos is Senior Lecturer and internationally recognised researcher in actuarial mathematics and actuarial modelling. His specific focus is on methods for performance measurement of pension funds, hedge funds and mutual funds as well as methods for predicting financial time series.

Co-supervisor – Dr Aline Villavicencio is Lecturer and internationally highly visible researcher on natural language processing. Her research is focused on multilinguality and lexical semantic aspects of language usage such as sentiment analysis, user profiling and modeling and identification of idiomaticity.

Applicants will be asked to provide: a covering letter with a personal statement why you are interested in conducting a PhD in text analytics, a detailed CV, full transcripts of any undergraduate or master’s programmes, half a page draft of a research proposal and one reference.

At a minimum, the successful applicant will have a good honours BSc degree in computer science, data science or other related subjects. An MSc with Merit or Distinction is desirable. Furthermore, strong analytical and mathematical skills are required, as well as good programming skills. Knowledge of natural language processing and machine learning are desirable but not essential.

For more information and details on how to apply please follow this link

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