Job description

Up to three full-time positions as associate professor in machine learning (ML) are available at the Department of Informatics (IFI) within the Section for Machine Learning. The department seeks potential scientific leaders with clear research visions, educational and administrative skills, and abilities to establish and lead research projects, who will strengthen our research and education within ML and associated application areas.

The positions form part of a vibrant and expanding ML research environment at IFI. The main subject areas of ML-driven research within the department currently are image analysis, language technology, robotics and intelligent systems, and bioinformatics. However, IFI also has a strategic ambition to expand and strengthen its foundational research in ML, intended to have synergies not only with the existing ML-based research groups but also with other researchers at the department and the newly founded Centre for Computational and Data Science (dScience).

The three hired candidates will join at an exciting point in time, with opportunities to shape the ML environment at the department as well as the cross-disciplinary dScience centre at the University.

Candidates are expected to take part in teaching and developing a growing portfolio of machine learning courses within the department. About 50% of the working time will be devoted to teaching and teaching-related activities and administrative tasks. The department offers classes at the bachelor, master, and PhD levels. Lectures and tuition are given in English and Norwegian (for most bachelor classes).

Specific information about the profiles of the three positions

Broadly speaking, all three positions should represent core expertise in contemporary machine learning methods and applications.

Candidates will be evaluated with respect to three different profiles, distinct for each of the positions:

1. For one of the positions, we are looking for a candidate with a strong track record in analyzing aspects of machine learning methodology related to ethical considerations, like transparency, reliability, bias, privacy, fairness, sustainability, or others.

2. One position is expected to be offered to a candidate with a strong background from more foundational research within deep learning and neural approaches with relevance beyond specific application areas.

3. Finally, one of the positions will be associated with one of the following established application areas within the department: (i) artificial intelligence methods for robotics and intelligent systems, or (ii) bioinformatics. For applications targeting this profile it will be important to document relevant background in the respective area.

The candidates must clearly state which of the three position profiles they are applying to, and if relevant (profile 3) also state the corresponding subject area(s) that they are interested in.

Qualification requirements

Primary assessment criteria will be as follows:

• Candidates must have a degree equivalent to a Norwegian PhD in a relevant area for the position.
• Candidates must have a strong research background in machine learning, either with respect to foundational issues within ML and/or applications of ML.
• Candidates for positions 1 and 2 will be expected to interact constructively with existing research groups at the department and are encouraged to indicate potential connections between their own specializations and others at the department.
• A promising publication record is required. Candidates must have international publications in acknowledged publication channels. The assessment of publications will emphasize originality, quality, and scope. Research output from the previous 5 years will be given weight. Candidates must have prior teaching experience within ML or associated application areas and have a commitment to engage in undergraduate and graduate educat ion and in mentoring and advising master and doctoral students.
• Pedagogical qualifications and teaching and supervision experience at all levels will be an important factor in the evaluation process and should be well documented in a teaching portfolio. The applicant should describe her/his qualifications in view of the Scholarship of Teaching and Learning (SoTL) framework which includes:
  • Focus on student learning
  • A clear development over time
  • A researching approach
  • A collegial attitude and practice

• The person appointed must have fluent oral and written communication skills in English.

The successful candidate who at the time of appointment cannot document sufficient teaching qualifications (minimum formal requirement is a 200 hrs pedagogical programme) will be required to obtain such qualifications within a two-year period.

The successful candidate must demonstrate mastery of both English and one of the Scandinavian languages as working languages. If an appointee is not fluent in a Scandinavian language, the appointee will be required to learn sufficient Norwegian within a two-year period, to be able to participate actively in all functions the position may involve.

The successful candidate should have an international profile with respect to the above criteria. The candidates for these positions will be selected based on excellence and fit with the research strategy of the ML section at the department.

Interviews will be part of the appointment process, along with a trial lecture.

Desirable qualifications:

• Since the field of machine learning is rapidly evolving, versatility and a proven ability to adapt the research focus is considered an advantage.
• Candidates should also be able to lead, conduct and collaborate in research projects. A strong track record in academic leadership is an advantage, as well as experience in the acquisition of research grants.
• International network, outreach activities, collegiality, and the ability to create a good working environment will be part of the evaluation.
• Experience in cross-disciplinary collaboration and collaboration with industry is an advantage.
• The person appointed will be requested to take on some administrative duties. Hence such experience will be an advantage.

Personal skills

• Ability to create an attractive, inclusive and competitive research environment
• Networking skills, ability to collaborate and conduct scientific leadership
• Ability to cooperate and communicate well with other members of the staff

We offer

• Salary from NOK 694 400 – 864 100 depending on competencies, in the position of Associate Professor (position code 1011)
• A professionally stimulating working environmen
• Attractive welfare benefits and a generous pension agreement, in addition to Oslo’s family-friendly environment with its rich opportunities for culture and outdoor activities
• The opportunity to apply for promotion to full professorship at a later stage

How to apply

The application must include:

• Application letter
• A research position paper (1-4 pages) describing the applicant’s vision and ambitions for the nearest future
• A detailed CV, including complete list of education, positions, pedagogical experience, administrative experience, project acquisition and coordination experience, funding and awards, and other qualifying activities
• Copies of educational certificates, PhD diploma, transcript of records and letters of recommendation
• A complete list of publications and academic merits and awards (if not included in the CV)
• Full text of up to 10 selected publications the applicant wishes to include in the evaluation
• A document discussing the importance, interrelation, and relevance of the selected papers for this position
• Description of dissemination activities beyond scientific publications
• Educational portfolio of 3–6 pages documenting educational competence and experience, including a reflection note in which your own teaching practice and view of learning is anchored in the SoTL framework. The teaching portfolio is expected to include:
  • Listing of teaching experience as well as work on developing/ revising/ renewing study plans and the design of courses
  • Documentation of teaching qualifications and supervision of Master and PhD students
• List of 3 reference persons (name, relation to candidate, e-mail and phone number)

Application with attachments must be submitted via our online recruitment system. Please note that all documentation must be in English or a Scandinavian language.

Formal regulations

As a general rule an interview will be used in the appointment process, usually supplemented with a trial teaching session. The basis for assessment will be the scientific production of the applicant, the teaching portfolio, pedagogical and educational qualifications, the applicant’s qualifications within leadership and administration, other qualifications as well as general personal suitability for the position. In ranking the competent applicants, the full range of qualifications will be considered and explicitly assessed.

Please refer to the Rules for Appointments to Professorships and Associate Professorships at the University of Oslo, the Guide for Applicants and Members of the Assessment Committee and Rules for practicing the requirement for basic pedagogical competence at the University of Oslo.

Pursuant to section 25(2) of the Freedom of Information Act, information concerning the applicant may be made public, even if the applicant has requested not to appear on the list of applicants.

The University of Oslo has a transfer agreement with all employees that is intended to secure the rights to all research results etc.

The University of Oslo has a personnel policy objective of achieving a balanced gender composition and to recruit people with an immigrant background.

In addition, the University of Oslo aims for its employees to reflect the diversity of the population to the greatest degree possible. We therefore encourage qualified applicants with disabilities or gaps in their CV to apply for the position. The University of Oslo will adapt the workplace to suit employees with disabilities. Applicants who indicate that they have disabilities or gaps in their CV are made aware that this information may be used for statistical purposes.

Contactinformation

• Associate professor Erik Velldal, email: erikve@ifi.uio.no, tel. +47 22840119
• Professor Andreas Austeng, email: andrea@ifi.uio.no, tel. +47 22852741

For technical questions about the recruitment system, HR Adviser Torunn Standal Guttormsen, email: t.s.guttormsen@mn.uio.no, tel. +47 22 85 42 72.

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