- Levelhulme Research Fellow In Novel Approaches For Constructing Optimised Multimodal Data

Tech
2016-08-07 23:00:00

Levelhulme Research Fellow in Novel Approaches for Constructing Optimised Multimodal Data

Deadline

Value of Scholarship

Level of Study

Country Tenable

Renewable

Levelhulme Research Fellow in Novel Approaches for Constructing Optimised Multimodal Data

Campus: Clifton

Additional Information: Fixed-term from 3 October 2016 until 2 October 2019

We are seeking to appoint a Post-doctoral Research Fellow who will work on a Leverhulme Trust funded research project entitled “Novel Approaches for Constructing Optimised Multimodal Data Spaces”. The post is offered as a fixed term, full-time appointment for 3 years to start on the 3^rd October 2016 (or very soon thereafter).

Aim of the project
This project aims to develop innovative approaches for constructing optimised multimodal data spaces. These approaches will be embedded into novel data analytics frameworksfor performing data science tasks with particular emphasis on data classification and clustering.

The objectives of this grant are to:

Analyse various types of multimodal data including bioinformatics data, sensor data, and image data in order to develop multimodal data analytics frameworks for these types of datasets.
Develop new versions of metaheuristic optimisation algorithms for constructing optimised multimodal data spaces. Create mathematical formulations as necessary.
Develop a Deep Learning algorithm for performing various classification and clustering tasks.
Conduct experiments to evaluate various multimodal data analytics frameworks, compare a selection of metaheuristic optimisation, classification, and clustering methods.
Implement the proposed multimodal data analytics framework into a user-friendly web-based application in order to make the algorithms widely accessible.
Produce publications in high impact factor journals.
Work closely with PhD students who will be contributing to this workflow and in particular on bioinformatics data, sensor data, body posture data and eye gaze data.

The successful applicant will have a PhD in the subject areas: computer science, computational intelligence, machine learning, data science, applied mathematics, optimization, or operational research. Some experience with metaheuristic optimisation methods, quantitative research and analysis, and applied mathematics is essential. Programming in R, Python or Matlab would be an advantage. The ability to work and deliver to specified deadlines is also essential.

Enquiries should be addressed to the Principal Investigator of The Leverhulme Trust Research Project Grant Dr Georgina Cosma (georgina.cosma@ntu.ac.uk) or +44 (0)115 848 3472 (please leave a message and I will get back to you).

Interviews w/c: 12^th September 2016

Applications from candidates who require sponsorship to work in the UK will be considered alongside other

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