About the Project
The PhD will be based in the School of Creative Technologies and will be supervised by Dr Mel Krokos, Dr Jiacheng Tan and Professor Kazuya Koyama. The project will be linked with our participation in a flagship EU Horizon 2020 project and involves working within a large international network of prestigious research collaborators and institutions.
The work on this project will broadly consist of:
-Enabling the processing of big data from emerging instrumentation infrastructures; data from the field of astronomy will be used as demonstrators;
-Facilitating remote visualisation and analysis of big data volumes through client-server models exploiting HPC facilities;
-Underpinning analysis of big data pipelines through machine learning algorithms; and finally
-Identifying synergies for the repurposing and application of the resulting framework to other big data domains.
Big data pipelines are increasingly encountered in many scientific domains, e.g. in astronomy there is an emerging flurry of enormously large, incredibly rich and highly complex data volumes expected from a new generation of telescope infrastructures. Such huge data volumes impose extremely challenging demands on traditional data management and analysis tools, requiring their adaptation, if not complete redesign, to run at scale with satisfactory performances.
The project vision is to pioneer development of a first of its kind framework for a new generation of high-capacity tools underpinning fully automated big data pipelines through real-time machine learning and creative visualisation. The development will be informed by work from our participation in a flagship EU Horizon 2020 project. The framework will be built upon earlier research together with an international network of renowned institutions from Germany, Australia and the UK. The work will be highly multidisciplinary with potential opportunities for paid student placements within these institutions.
Our framework will be flexible, allowing repurposing and application also to domains outside astronomy. It will also be rich enough to support tools capable of unlocking the intrinsic data value at the different stages of time evolving pipelines. The aim is to enable realisation of effective software stacks for high performance computing systems that work in a fully integrated and automated way with advanced creative visualisation tools, possibly with extensions to virtual reality.
The tools will display results to end users in real time and support appropriate feedback-generation mechanisms. They will also be extensible and scalable to next generations of data volumes. The results will be validated by a professional user community within our network of stakeholders. This will require a continuous interaction with all the members of our multidisciplinary international research team, who may want to offer funded visits and/or student internships.
General admissions criteria
You will need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate area. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency is required to be at a minimum of IELTS band 6.5 with no component score below 6.0.
Specific candidate requirements
Ideally, you should have a degree in any of the following disciplines with an emphasis on a strong coding background: computer games/animation, creative computing, digital media technology, information technology, virtual and augmented reality or engineering. Experience with HPC methodologies, as well as visualisation and deep learning algorithms would be advantageous. It is envisaged that travel to our overseas partners may be required at some point during the run of this project, e.g. to take up an internship, so it is essential that the successful candidate is able and willing to undertake international travel once the current pandemic restrictions are lifted and it is safe to travel.
How to Apply
We’d encourage you to contact Dr Mel Krokos (email@example.com) to discuss your interest before you apply, quoting the project code CCTS4510920 and the project title.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please note, to be considered for this PhD opportunity you must quote project code CCTS4510920 when applying.
Funding Availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
Funding during this project may also become available in the form of stipends and/or fully funded internships with our external partners in the UK, Germany and Australia.
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £15,100 p/a*
International part-time students: £7,550 p/a
*Fees are subject to annual increase
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