- Type of research degree
- Application deadline
- Ongoing deadline
- Country eligibility
- UK only
- Source of funding
- External organisation
- Dr Evangelos Pournaras
- School of Computing
- Research groups/institutes
- Distributed Systems and Services
Can you envision a more inclusive and direct democracy for our digital society empowered by an ethically-aligned AI and blockchain? The Distributed Systems and Services group at University of Leeds has an opening for a PhD candidate position in the lab of Distributed and Intelligent Social Computing Systems.
<p>As a PhD candidate in this project you are expected to develop and study decision-support systems using blockchain and distributed AI for multi-agent systems (reinforcement learning, distributed combinatorial optimization). You will apply these decision-support systems to mobile crowd-sensing platforms and digital voting systems to empower trustworthy collective decisions in Smart Cities. You will apply data science skills to data collected from citizens to understand collective crowd behavior. You will have a unique opportunity to apply this research in real-world by running pilots tests in the city of Aarau in Switzerland with a strong tradition on direct democracy initiatives. This project will run in collaboration with ETH Zurich and University of Fribourg in Switzerland as well as the city authorities of Aarau.</p> <p>Related projects and publications:</p> <p><a href="http://smart-agora.org">Smart Agora</a> </p> <p><a href="http://epos-net.org">EPOS</a></p> <p><a href="https://evangelospournaras.com/wordpress/wp-content/uploads/2020/07/Proof-of-Witness-Presence-Blockchain-Consensus-for-Augmented-Democracy-in-Smart-Cities.pdf">Proof of Witness Presence: Blockchain Consensus for Augmented Democracy in Smart Cities</a></p> <p><a href="https://evangelospournaras.com/wordpress/wp-content/uploads/2020/07/Proof-of-Witness-Presence-Blockchain-Consensus-for-Augmented-Democracy-in-Smart-Cities.pdf">Collective Learning: A 10-Year Odyssey to Human-centered Distributed Intelligence</a></p>
<p>Formal applications for research degree study should be made online through the <a href="https://eps.leeds.ac.uk/computing-research-degrees/doc/apply">University's website</a>. Please state clearly in the research information section that the research degree you wish to be considered for is “Distributed AI for Participatory Digital Democracy” as well as <a href="https://eps.leeds.ac.uk/computing/staff/6446/dr-evangelos-pournaras">Dr Evangelos Pournaras</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>
Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline and very good programming skills (covering data science and systems). An MSc in Computer Science or in the broader area of Computational Social Science would be desired, as well as an interest in decision and social sciences (mechanism design). Relevant industry experience is also recognised.
The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.5 overall with at least 6.5 in writing and 6.0 in each component (reading, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.
<p><strong>UK/EU</strong> - Studentship paying UK academic fees (£4,600 for Session 2020/21), together with a maintenance grant of £7,500 per year for 3.5 years. Funding is awarded on a competitive basis.</p> <p> </p>
<p>For further information regarding the application procedure, please contact Doctoral College Admissions,<br /> e: <a href="mailto:email@example.com">firstname.lastname@example.org</a>, t: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Evangelos Pournaras by email: <a href="mailto:E.Pournaras@leeds.ac.uk">E.Pournaras@leeds.ac.uk</a></p>
<h3 class="heading heading--sm">Linked research areas</h3>