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The Institute of Data Science at Maastricht University is seeking applications for 2 fully funded PhD student positions to develop computational methods for scalable and reusable knowledge graphs. These positions are supported through a Marie-Sklodowska-Curie Innovative Training Network for the project KnowGraphs: Knowledge Graphs at Scale (project #860801), which will recruit 15 PhD positions in total.
Knowledge graphs (KGs) are a flexible knowledge representation paradigm intended for knowledge capture and utilisation by humans and machines. Hence, they are key enablers for a number of technologies including question answering, personal assistants and artificial intelligence, and are used by several large companies including Microsoft, Google, Facebook, Amazon, Samsung, Ebay and IBM. However, substantive challenges exist, both technical as well as legal, in their formulation, interoperability, and deployment that preclude their widespread use. The overall objective of KnowGraphs is to scale knowledge graphs to be accessible to a wide audience of companies of all sizes, and to enable their use by end users across their professional and private life by using a multi-disciplinary and multisectorial approach.
The first position will focus on the development of new systems, methods, and specifications to create rich and indexable metadata in a semi-automated manner to enable the efficient discovery and reuse of knowledge graphs.
The second position will focus on approaches to automatically package, test, release, configure, deploy and monitor KGs across different storage solutions, access interfaces, and deployment environments.
Work with a multi-stakeholder team to discuss research problems;
Develop computational methods for the preparation, analysis, and dissemination of knowledge graphs;
Validate developed methods using synthetic and real world data;
Communicate the results of the research to stakeholders and target groups;
Write scientific papers for international peer-reviewed conferences and journals;
Present your work in international conferences;
Provide teaching assistance in undergraduate and master’s courses;
Participate in off-site internships;
Finalise the work in a PhD thesis.
MSc degree in Computer Science, Data Science, Artificial Intelligence or equivalent;
Training and/or experience with big data technologies, machine learning, deep learning, and data mining algorithms;
Demonstrable affinity with the topic;
Demonstrated ability to work independently as well as in a team;
Excellent oral and written English communication;
Relocate to Maastricht for the bulk of the work and to relocate within Europe for 2 short term internships with KnowGraph partners;
Complies with MC ITN Mobility Rule: Researchers must not have resided or carried out their main activity (work, studies, etc.) in the Netherlands for more than 12 months in the 3 years immediately before the recruitment date.
We offer a rewarding career at a young university in the heart of Europe, with a distinct global perspective and a strong focus on innovative research and education;
The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO), supplement with local UM provisions. For more information on terms of employment, please visit our website www.maastrichtuniversity.nl > Support > UM employees;
We offer a 4 year full-time appointment as PhD candidate. The first year will be a probation period, after a positive assessment the position will be extended for another 3 years. The earliest start date is April 1, 2020;
Remuneration will be according to according to standard salary levels for PhD students starting with a salary of €2.325,- with a yearly growth to €2.972,- gross a month, based on a full-time appointment (38 hours/week);
On top of this, there is an 8% holiday allowance and an 8.3% end of the year allowance;
We offer an attractive package of fringe benefits such as, reduction on collective health insurance, substantial leave arrangements, optional model for designing a personalised benefits package, sport facilities which staff may use at a modest charge and application for attractive fiscal arrangements for employees from abroad;
We provide a stimulating, collaborative and interdisciplinary work environment with excellent opportunities for professional and personal development;
Maastricht University has an excellent international reputation, and Maastricht itself is a beautiful, open-minded city located in the center of Western Europe.
Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 18,000 students and 4,300 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.
Data Science@Maastricht University is a pioneering, university-wide network of data-driven researchers with more than 25 years’ experience in data science education and research. Our community consists of multidisciplinary teams that address significant scientific problems and major societal issues through and related to data science including social, legal and ethical concerns in a wide range of academic disciplines. The Data Science@ UM community is expanding with at least 15 new positions in 2019.
The Institute of Data Science at Maastricht University (IDS@UM) is a recently established center led by distinguished professor Michel Dumontier. The mission of the Institute of Data Science is to foster an interfaculty environment for collaborative innovation in the development and application of data science technologies. IDS consists of a core team of data science experts that cooperate closely with researchers across disciplines such as medicine, life sciences, law, agriculture, social sciences and humanities, business and economics, knowledge engineering and smart services.
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