Umeå University, Faculty of Science and Technology

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

Are you interested in learning more? Read about Umeå university as a workplace

Umeå University, the Department of Computing Science (www.cs.umu.se), is seeking 4 PhD students in Computer Science with focus on AI for Data Management. Deadline for application is 31 October 2020.

Description of research group:
The Department of Computing Science is a dynamic environment with around 130 employees from more than 20 countries worldwide. The research will be carried out within the research group in “Artificial Intelligence for Data Management”, led by Prof. Diego Calvanese, well-known as one of the leading groups worldwide for its foundational and applied research at the interplay between Semantic Technologies, Knowledge Representation and Reasoning, and Databases.

The project is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. Software is the main enabler in these systems, and is an integrated research theme of the program.

The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.

Read more at: https://wasp-sweden.org/

The graduate school within WASP provides foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD students, researchers and industry. It thus provides added value on top of the existing PhD programs at the partner universities, providing unique opportunities for students who are dedicated to achieving international research excellence with industrial relevance.

Project description
The project led by professor Diego Calvanese concerns foundational and/or applied research in the context of flexible and efficient management of large amounts of richly structured data, by relying on the paradigm of Virtual Knowledge Graphs (VKGs, also known as Ontology-based Data Access). Specifically, the project aims at extending VKGs to address novel settings, that may include the following elements: (i) additional forms of data/knowledge (e.g., temporal, geospatial, aggregated, numeric); (ii) heterogeneous data sources of different types beyond relational ones (such as graph-structured data, json, xml, csv, streaming, textual), (iii) novel kinds of problems, notably provenance, explanation, privacy, machine learning for VKGs, optimization and performance tuning, updates, and evolution of data/knowledge, and (iv) novel applications of VKGs (e.g., industry 4.0, smart cities, e-health). Depending on the qualification and interests of the student, the PhD research topic can include one or more of the above extensions of the traditional VKG paradigm, and can be targeted more towards foundational and theoretical research or more towards applied and experimental research.

Admission requirements
The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad, or equivalent qualifications. To fulfil the specific entry requirements for doctoral studies in computing science, the applicant is required to have completed at least 90 ECTS credits in computing science, or in a subject considered to be directly relevant for the specialization in question. Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.

Candidates are expected to have very good knowledge in databases, data management and data modeling principles, or in semantic technologies and artificial intelligence (including knowledge representation and reasoning). For a more theoretically oriented PhD, candidates are also expected to have a solid background in the formal foundations of computer science (including mathematics, discrete mathematics, mathematical logic, theory of computation, possibly complexity theory). For a more applied PhD topic, candidates are also expected to have good programming skills and ideally experience in open-source and collaborative software project development.

Important personal qualities are good communication skills, including good skills in oral and written English, [SV1] good ability to co-operate and play in a team, ability to quickly grasp new concepts and place them in context, and creativity.

Terms of employment
The positions are aimed for PhD studies and research during four years, leading to a PhD exam. They are mainly devoted to postgraduate studies (at least 80% of the time), but may include up to 20% department service (usually teaching). If so, the total time for the position is extended accordingly (up to maximum five years). Expected starting date is January 2021, or as otherwise agreed.

 

Application

A complete application should contain the following documents:

  • a cover letter including a description of your research interests, your reasons to apply for the position, and your contact information;
  • a curriculum vitae;
  • reprints / copies of completed BSc and/or MSc thesis and other relevant publications, if any;
  • copies of degree certificates, including documentation of completed academic courses and obtained grades;
  • contact information for two persons willing to act as references;
  • documentation and description of other relevant experiences or competences.

The application must be written in English (preferably) or Swedish. Attached documents must be in Word or pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 31 October 2020.

The Department of Computing Science values the qualities that an even gender distribution brings to the department, and therefore we particularly encourage women to apply for the position.

The procedure for recruitment for the position is in accordance with the Higher Education Ordinance (chapter 12, 2§) and the decision regarding the position cannot be appealed.

For additional information, please contact professor Diego Calvanese diego.calvanese@umu.se .

We look forward to receiving your application!

Type of employment Temporary position
Contract type Full time
First day of employment 210101
Salary Månadslön
Number of positions 4
Full-time equivalent 100
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-1165-20
Contact
  • Diego Calvanese, diego.calvanese@umu.se
Union representative
  • SACO, 090-786 53 65
  • SEKO, 090-786 52 96
  • ST, 090-786 54 31
Published 15.Sep.2020
Last application date 31.Oct.2020 11:59 PM CET

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