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Research Fellow on CrowdSourcing and Linked Data - Southampton UK

Closing Date:  Wednesday 29 March 2017
Reference:  842617FP 

About the role

Candidates should have demonstrable experience (via publications in A and A* conferences and journals, participation in research projects, organisation of workshops, and similar) in Web and data science, with a particular focus on crowdsourcing. The position is for one year.

The candidate should have a PhD* in Computer Science or a related field and possess outstanding skills and experience in the area of crowdsourcing. Knowledge of Linked Data and Semantic Web technologies is a plus.  As a fundamental requirement, candidates must be able to communicate and write in fluent English and be willing to contribute to  collaborative projects. This includes both joint work with other institutions and meetings with the project team (quarterly or similar) in locations across Europe.

About the QROWD project

The increased data availability on transportation, traffic, roads, and mobility,  from real-time traffic sensors and CCTV streams to social media posts and crowdsourced maps, could greatly improve road safety, help reduce CO2 emissions, shorten commutes and deliveries, and ultimately enhance the quality of life in any European city. QROWD is an EU-funded pioneering initiative aiming at creating a socio-technical platform for local authorities, service providers and citizens to collaboratively take advantage of this wealth of Big Data sources. The platform will collect, curate and analyse multiple Big Data sets, harmonizing the heterogeneity of data formats employing Linked Data tools and standards. It will offer a range of core capabilities that support decision making and enable the participatory design of novel and location-based apps and transportation policies

QROWD's main outcomes are

  • Open-source software tools to support the five phases of the Big-Data Value Chain, combining machine-driven methods performance with the knowledge and skill of the crowd intelligence. Particular attention is paid to Linked Data fusion and interlinking.
  • Develop a platform to provide an interface for QROWD's algorithms and tools as a set of standalone configurable components. Researchers and developers will be able to configure their crowdsourcing services, monitor them, and seamlessly use their results to make algorithms smarter.
  • Showcase our technology through the delivery of two pilots - one with a focus on policy and decision makers in local government  in partnership with the city of Trento in Italy; and a second one targeted at service providers in the transport space, in partnership with TomTom, a global leader in navigation.

Proposed research topics related to the position are:

  • Improve machine learning algorithms for Linked data fusion and interlinking with crowdsourcing techniques
  • Develop novel methods of participatory sensing in the context of smart transport services based on Linked Data
  • Develop a Crowdsourcing task generator interface: Given a high level description of a task, guide the user to choose the most appropriate platform and combination of parameters for it.

Detailed information about how to apply can be found in the following link: