Funded PhD Opportunities 2012
Applications are invited for the PhD research projects listed below. Applicants should hold (or be expecting) a first class or good 2.1 degree in a relevant subject. For project-specific eligibility criteria and funding details please contact the academic responsible for the project before applying; their email addresses are listed below.
To apply, please use the University's online application form at http://www.shef.ac.uk/postgraduate/online and provide the name of the academic (listed below) when asked to name a supervisor.
The following projects are fully funded and available immediately
Runtime Analysis for Bio-Inspired Computation
Contact Dirk Sudholt (d.sudholt@cs.bham.ac.uk)
Bio-inspired algorithms include general-purpose metaheuristics like evolutionary algorithms, ant colony optimisation and particle swarm optimisation. These algorithms are constantly being applied successfully in various engineering disciplines. The goal of runtime analysis is to rigorously estimate the expected time until such an algorithm finds a good or optimal solution for interesting problems. This is done using mathematical techniques from the analysis of randomised algorithms, probability theory and computational complexity. This studentship offers a valuable opportunity to work within a very active, successful, challenging and widely respected field in the intersection of computational complexity and metaheuristics. The topic can range from evolutionary algorithms to ant colony optimisation and particle swarm optimisation, including hybrid and parallel variants thereof. In addition, there are opportunities for rigorously analysing biological models in collaboration with the Computational Systems Biology group.
Optimal Collective Decision-Making
Contact James Marshall (James.Marshall@sheffield.ac.uk)
This PhD will investigate optimal collective decision-making, applying statistical decision theory to the study of decision-making in biological systems from single cells to populations of social insects or humans. Since the perspective of the PhD is in terms of optimal decentralised control algorithms, links with collective robotics or other engineering applications may be investigated. A representative recent paper from this research programme is: http://www.sciencemag.org/content/early/2011/12/07/science.1210361.full.pdf and a report on it: http://www.sciencemag.org/content/early/2011/12/07/science.1216563.full.pdf
Machine Learning Methods for User Modelling and Personalised Summarisation
Contact Kalina Bontcheva (k.bontcheva@dcs.shef.ac.uk)
The project will develop machine learning methods to better capture information about the user from their social media activities and then use that information to summarise relevant new social media content. This research topic falls in the broader area of Natural Language Processing (NLP), where Sheffield University has established an internationally-leading reputation. In particular, through their widely-used GATE NLP toolkit (http://gate.ac.uk) which provides many indispensable tools for working with large unstructured text collections, including semantic search, information extraction and translation. Candidates should excellent computer programming skills. Experience with machine learning techniques for natural language processing is essential, and detailed knowledge of text summarisation and/or user modelling would be highly desirable.
Spiking Neural Networks and Synaptic Plasticity
Contact Eleni Vasilaki (e.vasilaki@dcs.shef.ac.uk)
A PhD position is available in the groups of Machine Learning & Computational Biology, under the supervision of Dr Eleni Vasilaki. The successful candidate will work in the field of Spiking Neural Networks and Synaptic Plasticity. This position is part of a larger European consortium, NAMASEN (http://www.namasen.net) with the participation of 12 academic and industrial partners. Details about the specific PhD position (Early Stage Researcher) can be found in the documentation of the NAMASEN web site. The position comes with very competitive salary and travel money, as well as the possibility of extended visits to the participating groups.
Modelling the Intelligibility of Speech in Real Environments
Contact Jon Barker (j.barker@dcs.shef.ac.uk)
This PhD position will be part of a larger European project, INSPIRE involving 10 European research institutes and 7 associated partners (http://www.ru.nl/clst/projects/speech/INSPIRE/). The network has been set up to study the intelligility of speech in real world environments. The senior researchers in the network are academics in computer science, engineering, psychology, linguistics, hearing science, as well as R&D scientists from leading businesses in acoustics and hearing instruments, and ENT specialists. The project at Sheffield will use computational models of hearing to better understand how listeners integrate information arriving at the left and right ear when listening to speech in background noise. The position comes with very competitive salary and travel money, as well as the possibility of extended visits to the participating groups. Candidates should be able to demonstrate a background in one or more of programming, mathematical modelling or signal processing. Prior exposure to courses in speech, hearing or linguistics would be an advantage.
Natural Speech Technology
Contact Thomas Hain (t.hain@dcs.shef.ac.uk)
Up to three funded Ph.D. studentships are available in the Speech and Hearing Research Group in the Department of Computer Science, University of Sheffield UK. These studentships are supported by the EPSRC programme grant in Natural Speech Technology (http://www.natural-speech-technology.org/) and associated funding sources. Project topics will be defined within the following areas: techniques for unsupervised learning from continuous streams of speech data; models that adapt to new scenarios and speaking styles; recognisers that can detect "who spoke what, when, and how" in any acoustic environment and for any task domain; PALs – Personal Adaptive Listening systems for people with communication disorders.