ALIGNED Project Post Doctoral Research Fellow
Start date: 1 Feb 2015
Contact: Rob Brennan (firstname.lastname@example.org)
The successful candidate will be part of a close-knit computer science/digital humanities research and software-dev team of 5 people, developing the Dacura platform to support the compilation of the Seshat Global History Databank (http://evolution-institute.org/seshat). The Dacura platform will enable historical and archaeological experts to create and manage structured linked data time-series datasets describing historical social evolution. Our aim is to make this the leading unified reference dataset for archaeological and historical time series data. Our research outputs will impact the state of the art in Linked Data for Humanities and Social Sciences, web data lifecycles/data quality metrics and software engineering tools for big data systems. The research is funded by the new European Commission Horizon 2020 project ALIGNED (http://www.aligned-project.eu/). ALIGNED is co-ordinated by the School of Computer Science and Statistics at Trinity College Dublin and is an affiliated project of the SFI CNGLII/ADAPT research centre. This is an applied research project, where we will primarily be focusing on applying research technologies to a real world, practical data-management problem.
The position will include some of the following responsibilities (depending on the skills, background and interests of the candidate):
• Developing web-based software tools on the Dacura platform to support Seshat data collection and management. (This is a key responsibility)
• Collaborating in the design and implementation of the core Dacura platform.
• Designing experiments and overseeing trial deployments.
• Helping to maintain and administer the Dacura platform, services and servers.
• Advance the ALIGNED project research agenda by participating in team work on:
o Enhancing Dacura with data-quality analytics, productivity metrics, workflows and automatic code generation to produce UI widgets.
o Design of Linked Data models to represent the Seshat data.
o The use of NLP and machine learning to extract structured datasets from semi-structured and unstructured web-sources.
o New models, methods and tools for integrating software and data engineering.
Skills and Experience
The ideal candidate will have the following qualifications, skills and experience:
• PhD in Computer Science or a related discipline (including maths, computer engineering)
• Excellent research track record in a relevant field (semantics, digital humanities, web technologies, statistical NLP, HCI, information retrieval)
• Strong Linux skills for server administration (Apache, Fuseki, Git)
• Knowledge Engineering experience using triple-stores, common vocabularies, RDF, RDFS, JSON-LD, OWL
In addition to the above core skills, any experience or skills in the following areas would be useful:
• Java and Python programming
• Track record of research skills such as writing and publishing high impact academic papers, conducting experiments, research proposal writing.
• Familiarity with one or more of: NLP techniques; scalability & performance; statistical analysis; formal methods; software engineering.
It is very important that the candidate should have the ability to collaborate constructively as part of a self-managing team. There will be 5 people working full-time on this project. Everybody will contribute to all areas of the team’s work, from administration through design and implementation to research. We will ensure that everybody on the team has the opportunity to advance their career, learn new skills and pursue relevant research.
The successful candidate will be offered a two year contract, remunerated at a level between €40,003k and €46,255k, depending on skills and experience. The Dacura team is actively seeking further funding and follow on projects in a growing research area, with exciting inter-disciplinary collaborations with high profile international scientists and scholars in the University of Oxford, University of Leipzig and Santa Fe Institute.