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<td>4 PhD places in vision and robotics in Edinburgh</td>
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<td>Sat, 25 Jul 2015 08:23:16 +0100</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">From: </th>
<td>Bob Fisher <a class="moz-txt-link-rfc2396E" href="mailto:rbf@inf.ed.ac.uk"><rbf@inf.ed.ac.uk></a></td>
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<td><a class="moz-txt-link-abbreviated" href="mailto:wp@cg.tuwien.ac.at">wp@cg.tuwien.ac.at</a></td>
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Edinburgh and Heriot-Watt universities have 4 fully funded 4-year PhD industrial
studentships with Schlumberger (oil and gas), RSSB (Rail Safety Board),
Costain (Engineering solutions), and UoE (unmanned surface vehicles).
More details are below.
Key Features and Benefits
================
* Fully funded studentship covering Home/EU tuition fees and stipend (14,057 for 2015/16).
* Access to our world class infrastructure, enhanced through 6.1m EPSRC capital grant ROBOTARIUM.
* Students benefit from supervision by academic experts from both institutions and graduate
with a joint PhD from University of Edinburgh and Heriot -Watt University.
* Excellent training opportunities, including masters level courses in year one, supplemented
by training in commercial awareness, social challenges and innovation.
* Enterprise funds available to support development of early commercialisation prototypes.
* Starting from: September 2015
Entry and Language Requirements
================
* Applicants should have, or expect to obtain, a first-class degree in Engineering,
Computer Science, or related subjects.
* Non-native English speakers need to provide evidence of a SELT (Secure English Language Test)
at CEFR (Common European Framework of Reference) Level B2 taken within 2 years of the date of
application. The minimum requirement is IELTS 6.5 or equivalent, no individual component can
be less than 5.5 in a single sitting. A degree from an English speaking university may also
be accepted in some circumstances, but we do not accept TOEFL certificates.
Industrial partners
================
Schlumberger is the leading supplier of technology, project management, and
information solutions for oil and gas companies around the world. Through
their well site operations and in their research and engineering facilities,
they are working to develop products, services and solutions that optimize
customer performance in a safe and environmentally sound manner.
As automation of drilling processes is developed, operation will be split
between completely automated tasks and tasks that are carried out by humans.
The project will look at how teams comprising human and robotic actors will
collaborate to achieve complex and uncertain tasks in drilling operations.
Particular areas of interest include delivery/execution monitoring of collaborative
plans; developing/maintaining trust between human and automated parts of the system;
multi-modal interfaces for communication and coordination; dynamically changing
activities in response to unexpected events/changes in priorities; and reliable
state/event detection and communication mechanisms that prioritise significant
events and support effective human decision-making.
To find out more please contact: Professor David Lane (<a class="moz-txt-link-abbreviated" href="mailto:D.M.Lane@hw.ac.uk">D.M.Lane@hw.ac.uk</a>)
RSSB is a not-for-profit organisation whose purpose is to help members to continuously
improve the level of safety in the rail industry, to drive out unnecessary cost and to
improve business performance. ERTMS (the European Railway Traffic Management System)
and ATO (Automatic Train Operation) are changing the task of driving a train. This is
occurring at a time when automation of transport systems (e.g. automated passenger
pods at Heathrow airport, the Google Car, automated mining trucks etc.) is becoming
increasingly common through the convergence of low cost, high performance sensors,
communications and computing systems and the development of advanced code libraries
for extracting information from sensor data. With these factors in mind, it can be
expected that the way a train driver operates will be influenced by these developments
in order to achieve safer, more efficient and more frequent train services.
To find out more please contact: Professor Ruth Aylett (<a class="moz-txt-link-abbreviated" href="mailto:r.s.aylett@hw.ac.uk">r.s.aylett@hw.ac.uk</a>)
Costain is recognised as one of the UK.s leading engineering solutions providers,
delivering integrated consulting, project delivery and operations and maintenance
services to major blue-chip customers in targeted market sectors. Many repetitive
industrial tasks require significant cognitive load which results in operator
fatigue and in turn can become dangerous. The development of robotic sensing
technology and compliant feedback technology, will allow semi-autonomous robotics
systems to improve this type of work flow. This project aims to explore methods in
which a robotic system with shared autonomy can contribute to the operation of a
Kinesthetic tool (such as a piece of machinery) and in doing so reduce the cognitive
load and fatigue of the human operator. As this is an EPSRC iCASE (industrial CASE)
studentship, over the course of the four years, the student will be
required to spend at least 3 months at the sponsor's premises.
This project is only valid for UK students due to the nature of the funding.
To find out more please contact: Professor Sethu Vijayakumar (<a class="moz-txt-link-abbreviated" href="mailto:sethu.vijayakumar@ed.ac.uk">sethu.vijayakumar@ed.ac.uk</a>)
Intention-aware Motion Planning.Project only valid for UK students due to
nature of the funding. The goal of this industry sponsored project is to
research and extend previous techniques to give a new approach to categorising
motion and inferring intent to support robust maritime autonomy decisions in
Unmanned Surface Vehicles. Maritime systems have to manage high levels of data
sparsity and inhomogeneity to reason effectively in terms of the grammar of motion
adopted by different objects. Elements of topology-based trajectory classification
for inferring motion semantics and categorisation, distributed tracking & planning
with reactive models, Bayesian reasoning and learning algorithms will be combined and
extended for noisy data sampled on large spatiotemporal scales to give high-confidence
inference of intent to inform autonomous decisions.
To find out more please contact: Dr Subramanian Ramamoorthy (<a class="moz-txt-link-abbreviated" href="mailto:s.ramamoorthy@ed.ac.uk">s.ramamoorthy@ed.ac.uk</a>)
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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