We have an opening for a Research Assistant (Early-Stage Researcher) position as part of the ESSENCE (Evolution of Shared SEmaNtics in Computational Environments) Marie Curie Initial Training Network, a 4-year international collaborative research training project coordinated by the University of Edinburgh. This is a high-profile position that offers exceptional benefits ideally suited for top graduates.
A couple of PhD studentships are available in the computer networking area in the School of Informatics, the University of Edinburgh. The successful candidates will pursue a PhD degree in the area of computer networks, with particular focus on research topics in the hot and fast-growing area of Software-Defined Networking (SDN) and data centre networks. The team broadly aims to enhance SDN technologies and apply them to real-world problems in the computer networking area.
A central feature of human learning is our ability to transfer knowledge across different tasks and contexts. We reason by analogy, discover widely applicable principles and rules, and find new ways to represent and explain familiar phenomena. In the domain of causal learning, recent studies have shed light on human transfer learning and abstraction, but no formal model captures the fluency with which people explore new causal hypotheses and representations.
A fully-funded PhD opening in computer architecture is available in the School of Informatics at the University of Edinburgh. We are growing a new team to develop disruptive system-level technologies necessary to overcome the limitations of conventional computing platforms in the age of big data. You will conduct cutting-edge research to enable tomorrow's high-performance energy-efficient servers and datacenters for cloud applications like Facebook, Google Search, Siri, and MapReduce.
The ESSENCE (Evolution of Shared SEmaNtics in Computational Environments, www.essence-network.eu) Marie Curie Initial Training Network is offering six Early-Stage Researcher (pre-doctoral) positions, to start in September 2014. The application deadline for these posts is 24th April 2014.
Guesstimation is the technique of estimating approximate answers to numerical questions. GORT is an interactive guesstimation web service that combines proof planning (to work out what information must be combined to answer a question) with Semantic Web search (to find this information). It has been applied, for instance, to guesstimate questions about renewable energy, e.g., “How much would it cost to meet the UK’s entire electricity requirements from solar panels?”. GORT has previously been developed via a succession of four UG4 and MSc projects.
A PhD studentship is available to investigate the learning capabilities of fruitfly (Drosophila) larvae. The aim is to establish how the ability of these simple animals to associate stimuli is similar or different to vertebrates, using crucial experimental paradigms drawn from current learning theory, and to develop new algorithms for learning based on the neural circuits in the larval brain.
Applications are invited for a PhD studentship in testing concurrent programs and defining quality metrics for them. The PhD is fully funded for three years at UK/EU student fee level. The project will be supervised by Ajitha Rajan of the School of Informatics at the University of Edinburgh. The project will be in collaboration with Facebook, UK.
A PhD studentship is available to devise simulated and robotic models of fruitfly (Drosophila) larvae. The aim is to account for and replicate their motor patterns during exploration, with single and multiple sensory cues, and in particular how these are altered during associative learning.
The Centre for Intelligent Systems & their Applications (CISA) has pioneered mechanisms for representational change driven by reasoning failures. The Institute is particularly interested in going beyond belief revision to encompass changes of concepts, i.e., the language in which the beliefs are represented. Representational change is needed for agents working in a changing world with changing goals. It finds applications in areas as diverse as mathematics, formal verification, emergency response and on-line trading. Reformation is a new, general-purpose algorithm developed which uses conceptual change to either block unwanted inferences or unblock wanted ones. It has the potential to replace the more domain-specific techniques we have developed and to provide a theoretical underpinning for our work, thus increasing its robustness, predictability and breadth of application.