Assistant Professor in Machine Learning, with specialization in societal impact
Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has over 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.
Uppsala University’s tenure track system grants assistant professors the possibility to be assessed for promotion to the position as associate professor. The university provides support in various areas, such as scientific and educational development, leadership development as well as patent and innovation support.
The Department of Information Technology is situated in the newly built Ångström House 10, which contains a visualization studio, a social robot lab, a maker space and 3D printing workshop. Researchers work in all areas of IT, from designing processers, through HCI and cybersecurity, to cancer research tools and methods from numerical analysis and machine learning. Active researchers in Artificial Intelligence include Thomas Schön and David Sumpter (machine learning); Ida-Maria Sintorn and Caroline Wählby (AI and image analysis); Ginevra Castellano and Katie Winkle (social robotics); and Matteo Magnani (data science and networks).
Uppsala University’s has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research and the Centre for Interdisciplinary Mathematics.
The IT Department currently has about 300 employees, including 120 teachers and 110 PhD students. More than 4000 students study one or more courses at the department each year and teaching staff actively develop new approaches to education.
The position is an important part of the new Beijer Laboratory for Artificial Intelligence, which is part of the universities ambition to grow its activities within the subject of AI.
Description of subject area of the employment: Research in to how AI and machine learning is used in society. Potential research areas include methods for improving quality and documentation of large data sets; identifying limits to and biases in machine learning algorithms; algorithmic accountability; human-centred machine learning; novel applications in humanities; and doing social good with machine learning (and the limits of such approaches). The research should come from a technical viewpoint, but be grounded in applications in society.
- Teaching and research (between which the balance is initially 30/70 teaching/research), administration and public communication.
- Build an active group around research specialisation. The position is associated with funding for one PhD student and startup funds for travel etc.
- Teaching duties include course responsibility and course administration and supervision of second- and third-cycle students. We are looking, in particular, for candidates who can teach courses in statistical machine learning, neural networks, advanced probabilistic machine learning, reinforcement learning and are interested in developing a new course in societal impacts of machine learning. Supervision of Masters students is an important responsibility. The candidate should develop their own course in an area related to societal impact of machine learning.
- Engagement with a wider general public in questions about Artificial Intelligence and Machine Learning in society. Follow developments within the subject area and the development of society in general that is important for the work at the university.
Appointment Period: The position can be held for a maximum of six years. An Assistant Professor can apply for promotion to Associate Professor. If the Assistant Professor is deemed suitable and fulfills the criteria for promotion established by the Faculty Board he/she shall be promoted to and employed as Associate Professor.
- PhD in machine learning, statistics, engineering, mathematics or related area. PhDs in other fields, where the research work has focused on machine learning methods, are also eligible for this position. Applicants who have obtained a PhD degree or achieved the equivalent competence in five years or less prior to the end of the application period will be given priority.
- Research Expertise and Teaching Expertise. It is necessary that the pedagogical skills, the research expertise and the professional skills are relevant to the content of the employment and the tasks that will be included in the employment.
- Applicants should have completed teacher training of relevance to operations at the University, comprising five weeks, or have acquired the equivalent knowledge. If special circumstances apply, this training for teachers in higher education may be completed during the first two years of employment.
- Documented ability to teach in Swedish or English is a requirement unless special reasons prevail.
- Personal capabilities necessary to carry out fully the duties of the appointment.
Assessment Criteria/Ranking of applicants that fulfil the above-mentioned qualifications required
The ranking of eligible applicants will be based primarily on research and teaching expertise, of which weight will be primarily given to research.
Research Expertise comprises research merits as well as the applicant´s potential to contribute to the future development of both research and teaching. In assessing research expertise research quality must be the prime consideration. The scope of research, primarily in regard to depth and breadth, must also be afforded consideration. A qualitative assessment will be made of the research. In assessing research expertise special weight will be attached to research merits in technical aspects of machine learning with respect to social impact. Potential research areas include, but are not limited to: improving quality and documentation of large data sets; identifying limits to and biases in machine learning algorithms; algorithmic accountability and doing social good with machine learning (and the limits of such thinking); humanities-based applications of machine learning.
Teaching Expertise comprises educational and teaching qualifications. In assessing teaching expertise teaching quality must be the prime consideration. The scope of teaching experience, in terms of both breadth and depth, must also be afforded consideration. In assessing teaching expertise special weight will be attached to merits in teaching statistical methods used in machine learning, while adopting an approach that includes an understanding of how AI and machine learning impact society.
Collaboration Expertise is important and will be afforded consideration. Collaborative expertise is demonstrated by the ability and skill of planning, organizing and implementing interaction with the surrounding community. Popular publications, public debate and lectures are examples of forms of interaction with the surrounding community. Other examples of collaboration are patent applications, commercialization and industrial cooperation. The ability to translate knowledge sharing with the surrounding community into activities of importance to the education's development and quality is part of the collaboration expertise.
All merits must be documented in a manner that makes it possible to assess both quality and scope.
In filling this position the university aims to appoint the applicant who, following a qualitative holistic assessment of her/his competence and expertise, is judged to have the best potential to carry out and develop the relevant duties and to help advance operations.
In an overall assessment of the applicant’s qualifications, parental leave, part-time work relating to care of children, union assignments, military service, or the like are to be regarded as work experience.
Type of position: Temporary full-time position six years.
Salary: Individually negotiated salary.
Number of positions: 1
Working hours: 100%
Starting date: 2023-04-01 or as otherwise agreed
Please submit your application by February 8 2023, UFV-PA 2022/3956.
In the event of any disagreement between the English and the Swedish versions of this announcement, the Swedish version takes precedence.
If you are an international candidate, you will find a lot of information about working and living in Sweden at www.uu.se/joinus.
Please do not send offers of recruitment or advertising services.
Submit your application through Uppsala University's recruitment system.
Placement: Department of Information Technology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed salary
Number of positions: 1
Working hours: 100%
County: Uppsala län
Number of reference: UFV-PA 2022/3956
Last application date: 2023-02-08
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