[DMANET] PhD positions at Lancaster University

We are pleased to announce the opening of two PhD positions at Lancaster University, with the School of Computing and Communications. Lancaster University is ranked within the top 10 UK University and is with a vibrant and friendly environment.


Position 1
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PhD Studentship – Dynamic Heterogeneous Information Fusion, Patterns Extraction and Classification. From Data to Knowledge
Lancaster University, School of Computing and Communications,

Stipend: Minimum of £13590/year

The studentship offers a fully funded PhD studentship, including a stipend and university fees. Additional finance is available to provide equipment, consumables and project travel costs.

The problem of interest
Applications are invited for a 3.5-year PhD studentship in the area of sensor data fusion.

The research is funded by SELEX Gallileo, UK (www.selexgalileo.com) and will be conducted in joint collaboration with the industrial sponsor. The project is part of a Centre of Excellence comprising SELEX Galileo and 5 UK universities. The aim is of this studentship is to address both theoretical and application in extracting knowledge from a large amount of heterogeneous data, e.g. optical, infrared, Synthetic Aperture Radar (SAR) images and burst illumination laser. The main focus will be based on combinatorial pattern matching, statistical data mining methods for various purposes including tracking and decision making.

Methodology
Bayesian distributed data fusion methods and probabilistic graphical models (such as Bayesian networks, tree types of models), nonparametric inference methods will be studied. One approach that will be investigated is based on hierarchical Dirichlet models, and the impact of the prior on the decision making process, nonparametric models for inference and fusion. Nonparametric methods are a class of methods that allow the data to determine the complexity of the model. This prior knowledge will afford to account for prior information in an efficient way. Data of various types will be considered, such as from the airborne sensors, and ground sensors. The reliability metrics will include (but are not limited to) accuracy, computational time and minimum communications. Performance studies will be conducted between centralised, distributed and hybrid architectures. Some of the challenges that will be considered are: aggregation (fusion) of high dimensional data, both on-line and off-line, asynchronous arrival of the data, detection of faults and abilities to cope with missing or rare data, followed by an inference. In case of faults the sensor network should be reconfigured in a way to cope with the missing data and faults. Then the information extracted from the data is meant to be provided to the Unmanned Aerial Vehicles (UAVs) or operators for achieving better situation awareness. The results from the fusion algorithms will be used for recognition and classification of stealthy objects.

Areas of expertise/ qualifications: statistical and probabilistic methods, numerical methods, digital signal processing, optimisation. Candidates with solid mathematical background are invited to apply for this position. Applicants should hold (or expect to obtain) a minimum upper-second class honours degree or equivalent in a discipline related with electrical engineering, aerospace engineering, statistical science or computer science. Willingness to study across scientific disciplines and a willingness to learn fast new areas of research will also be essential. Because this studentship is a part of a project and includes communication with partners of a large consortium, excellent communications skills are required.

How to apply: To apply please complete a PhD application form available at
(http://www.lancs.ac.uk/study/postgraduate/ ) and indicate on the form that you wish to be considered for the PhD studentship sponsored by Selex Gallileo, UK. The following documents are needed: a CV, a cover letter (including a motivation statement), a research proposal, and the contact details of two referees. Further information can be obtained from Dr Mila Mihaylova (mila.mihaylova@lancaster.ac.uk) and on Tel: 01524 510388. The PhD student will be based in Lancaster University, Infolab21 – a Centre of Excellence in ICT at the School of Computing and Communication Systems (http://www.scc.lancs.ac.uk/). This provides great opportunities for professional and personal development.


Position 2
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PhD studentship - Analysis and Classification of Objects in Complex Environmental Systems (141)
Stipend: Minimum of £13590/year

The studentship offers a fully funded PhD studentship, including a stipend and university fees, as part of the Doctoral Training Centre Lancaster-Liverpool. Additional finance is available to provide equipment, consumables and project travel costs.

Applications are invited for a 3-year PhD studentship at Lancaster University.

This project aims to address some of the most challenging remote sensing problems of detection and analysis of environmental changes and events based on multiple sensor data. Far infrared and Synthetic Aperture Radar (SAR) imagery data will be integrated within a novel fusion framework. Different applications are envisaged to be considered, including for ground ecological systems, and interesting events detection and monitoring. The uniqueness of the project stems from the synergy of C++ software engineering, advanced image and signal processing techniques with decision making and real time systems aimed to cope with different noises and obscuration.

The project is of particular interest to the Company and will generate significant revenues as a number of existing customers already expressed their interest in the outcome of this project

Funding Notes:

Project supervision:
* Rinicom Ltd
* Lancaster University, UK

Application details
To apply for this opportunity please email graduate-applications@cgeinnovation.org with:

A completed Application Criteria document (.doc file)
2 page CV
2 page expression of interest

Reference number: 141
Informal inquiries can be sent to Dr Lyudmila Mihaylova (email: mila.mihaylova@lancaster.ac.uk).
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