Umeå University, Faculty of Science and Technology

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Umeå University welcomes applications for a tenure track position as Assistant Professor in Computer Science with focus on machine learning at the Department of Computing Science. The position, which is established through the Wallenberg AI, Autonomous Systems and Software Program (WASP http://wasp-sweden.org/), comes with a substantial recruitment package. 

Work description
The primary focus is research on advanced machine learning techniques. Examples of research areas are representation learning and grounding, sequential decision-making and reinforcement learning, learning from small data sets, Generative Adversarial Networks (GANs), incremental learning, and multi-task/transfer learning. Although the expertise should primarily be in the core of machine learning, the applicant should ideally also have experience from applying the research in some application areas.

The position offers stimulating and challenging tasks. A main criterion for employment is a strong commitment to research. The successful candidate will have an important role and large freedom in developing the area of machine learning at the department, both in research and in undergraduate and graduate education. The candidate is also expected to develop collaboration across institutional boundaries inside and outside the university and within WASP.

The position includes a substantial recruitment package including full funding of two PhD candidates (four years each) and two post docs (two years each). 

The position is for 5 years, of which 80 % is for research and 20 % for pedagogical qualification/teaching.

The position is part of Umeå University’s tenure track system, which means that an Assistant Professor has the right to apply for promotion to a position as Senior Lecturer/Associate Professor. 

Eligibility
Eligible candidates must have a doctoral degree in Computer Science/Computing Science, or equivalent scientific competence in an area of relevance for the position. In addition, relevant and documented scientific and pedagogical skills in the area of the employment is required. Applicants who have completed their doctoral degree or an equivalent degree no more than five years prior to the deadline for application will primarily be considered.

A high level of proficiency in both spoken and written English is a requirement.

Assessment criteria
In the selection of candidates, particular emphasis will be put on the degree of scientific skills.

Scientific expertise must relate to the core of machine learning, with focus on computational and method-oriented research. Documented experience from applying that expertise in an application area is an advantage.

The degree of scientific skills will be assessed on the basis of scientific work published in internationally well-respected scientific journals and conferences that apply a peer review system and also on the applicant’s documented ability to develop and conduct research projects, where the quality, originality, and timeliness of the research contributions will be assessed. The scientific skills will also be assessed from the enclosed research proposal, where the scientific height of the proposed research, the relevance that the proposed area of research has for the objectives of the employment, and the degree of fresh thinking will be assessed. Postdoctoral experience from academy or industry is a merit, as well as documented experience of applying the research within different areas of application. Finally, documented ability to competitively obtain research funding is a merit.

In addition, the level of teaching skills as well as the ability to develop and manage activities and staff will be considered. Furthermore, administrative and other skills of interest with respect to the subject matter and the tasks to be included in the employment will be considered, as well as the ability to interact with the surrounding community.

Application
The application, preferably written in English, should include:

  • A cover letter including contact information
  • A Curriculum Vitae containing academic and professional merits, including experiences from research and education, and a complete list of scientific publications
  • An account of research experiences (see instructions in link below)
  • An account of teaching experiences. (see instructions in link below)
  • An account of experiences of developing and managing activities and staff
  • An account of experiences regarding interacting with the surrounding society, and popularizing science
  • A research plan describing the research you plan to pursue in this position (max 5 pages), and how your research will contribute to the research within the strategic areas defined by WASP
  • Certified copies of relevant diplomas, grades and degree certificates
  • Copies of at most ten relevant publications, numbered according to the list of publications
  • Names and current contact information (position, affiliation, email address, and work phone number) of three reference persons.

Instructions for how to describe the account of scientific and educational activities can be found here:
https://www.aurora.umu.se/globalassets/dokument/enheter/teknat/for-vara-anstallda/personal-och-organisation/lektorsarenden/instructions-for-applicants-when-applying-for-a-teacher-appointment.pdf

The application is to be submitted electronically using the e-recruitment system of Umeå University, January 2, 2019 at the latest.

Information
Further information can be obtained from Head of Department Pedher Johansson, pedher@cs.umu.se, +46(0)90 786 7707. 

Type of employment Temporary position longer than 6 months
Contract type Full time
First day of employment Upon agreement
Salary Monthly salary
Number of positions 1
Working hours 100 %
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-1810-18
Contact
  • Pedher Johansson, +46 90-786 77 07
Union representative
  • SACO, +46 90-786 53 65
  • SEKO, +46 90-786 52 96
  • ST, +46 90-786 54 31
Published 27.Nov.2018
Last application date 02.Jan.2019 11:59 PM CET

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