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Postdoctoral Research Fellow position

At the Centre for the Science of Learning & Technology (SLATE), Faculty of Psychology there is a vacancy for a postdoctoral research fellow position within artificial intelligence and education. The position will also be associated with the new national AI LEARN Centre at SLATE. 

The position is for a fixed term of four years, with the inclusion of career development work (e.g., teaching, project work) accounting for 25% of the entire period, and is financed by University of Bergen. 

The position is open to an incoming candidate, se LEAD AI mobility rules. 

About the project/work tasks:

The Centre for the Science of Learning and Technology (SLATE) investigates the technological, pedagogical, cultural, ethical, and legal aspects of learning analytics (LA) and artificial intelligence in education (AIEd) and promote the responsible use of technology in education. The recent release of generative AI tools has led to their adoption in research, a phenomenon that is not yet well understood. Together with possibilities of creativity, productivity, and new multimodal tools to support knowledge creation, fundamental questions of epistemic risks and illusions of understanding of scientific knowledge arise. 

This position will help us to advance our knowledge about how humans learn to trust AI systems, including ways in which they can fruitfully critique and challenge them. The successful candidate will work closely with Professor Kitto to build up interdisciplinary linkages across SLATE and the broader UiB context. They will contribute to significant lines of work within the new national Artificial Centre for the Empowerment of Human Learning  (AI Learn), which has a focus on Human-AI hybrid intelligence,  and support ongoing collaborations with the Australian funded cross institutional project on Democratising AI. 

Indicative research topics:

• Developing tools and services through Design Based Research that will support educational stakeholders to interrogate and challenge vendors of AI based systems and products
• Robust methods for developing games and web services that can be used to support parents, teachers and students to understand and compare different AI systems and services
• Decision support services to help educational decision makers (e.g. principals and government officials) ask creative and critical questions about the ways in which AI tools work and how they might be misused or abused (both intentionally and unintentionally)
• Boundary objects that can be used to bridge the divide between educators and AI experts (e.g. causal DAGs, hybrid forums, games) so helping people to understand AI without undergoing formal education
• Supporting educational stakeholders to understand the data generated by AI tools such that they can better monitor and control them

NB: The list above is not comprehensive. Applicants are encouraged to propose a something different after reading through the resources collected by different projects that this position will work upon. Suggested reading to explore this line of research further:

1. Kitto, K., Hicks, B., & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory. British Journal of Educational Technology, 54(5), 1095-1124. 
2. Swist, T., Gulson, K. N., Benn, C., Kitto, K., Knight, S., & Zhang, V. (2024). A technical democracy design experiment: Making the UK exam algorithm controversy game. Design Studies, 91, 101245.
3. Hicks, B., Kitto, K., Payne, L., & Buckingham Shum, S. (2022). Thinking with causal models: A visual formalism for collaboratively crafting assumptions. In LAK22: 12th international learning analytics and knowledge conference (pp. 250-259).
4. Callon, M., Lascoumes, P., & Barthe, Y. (2011). Acting in an uncertain world: An essay on technical democracy. MIT press. 
5. Thompson, G., Gulson, K. N., Swist, T., & Witzenberger, K. (2023). Responding to sociotechnical controversies in education: A modest proposal toward technical democracy. Learning, Media and Technology, 48(2), 240-252.
6. Kitto, K., & Gibson, A. (2024). Places to intervene in complex learning systems. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 929-935).

 

About the LEAD AI fellowship programme
LEAD AI is the University of Bergen's career and mobility fellowship program for training 19 postdoctoral fellows in artificial intelligence.

The program has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101126560.

The LEAD AI programme offers high-quality inter- and transdisciplinary research and training opportunities in the area of artificial intelligence supported by a dedicated supervision and mentoring, encouraging inter-sectoral exposure, in particular

• academic freedom
• benefits from knowledge and skills transfer between disciplines, organisations and sectors
• structured, skill-based training
• high-quality working conditions
• personal career support
• equal opportunities

For more information see the LEAD AI webpage or send an email to leadai@uib.no 

Qualifications and personal qualities:

• Applicants must hold a PhD degree according to Norwegian standards. A strong preference is for applicants with a background in experimental psychology or HCI, but other applicants will be considered. Applicants will be considered if they have submitted his/her doctoral thesis for assessment prior to the application deadline. It is a condition of employment that the PhD has been awarded. 
• Applicant should have a genuine interest in AI the learning sciences, and the research proposal must be related to end users to understand and use artificial intelligence effectively and ethically.
• Applicants cannot previously have been employed as a postdoctoral fellow at UiB and they cannot be employed by any other institution for the time of the fellowship.
• Experience with AI-related research and/or innovation is an advantage. 
• The LEAD AI mobility rules must be followed.
• Applicants must be able to work independently and in a structured manner and have the ability to cooperate with others.
• Applicants must have excellent skills in oral and written English. 
• The application and relevant documents must be in English.

Personal and relational qualities will be emphasized. Research experience, ambitions and potential will also be considered during candidate evaluation.

Special requirements for the position
The University of Bergen is subjected to the regulation for export control system. The regulation will be applied in the processing of the applications.  

About the position of postdoctoral research fellow:

The postdoctoral position is a fixed term position with the primary objective of qualifying the appointee for work in top academic positions. The fixed-term period for this position is four years. Individuals may not be hired for more than one fixed-term period as a postdoctoral research fellow at the same institution. 

For all LEAD AI fellows, a Personal Career Development Plan (PCDP) will be developed jointly by the fellow, supervisor, and co-supervisor by the end of Month 3 of the fellowship, including a plan for the individual research budget, and information on additional funding where applicable.

It is a requirement that the project is completed in the course of the period of employment.

What we offer:

• An engaged and professionally stimulating working environment
• Position as postdoctoral research fellow (code 1352 in the basic collective agreement) and a gross annual salary of NOK 657 000 upon appointment. Further increases in salary are made according to length of service in the position. A higher salary may be considered for a particularly well-qualified applicant.
• Welfare benefits* and social benefits including pension-saving in the Norwegian Public Service Pension Fund, occupational injury insurance, full salary during sick leave for 52 weeks, and paid parental leave**.
• Extension of the position term (work contract) due to sick leave and parental leave.
• Norwegian language courses free of charge.
• High standards for working hours, holidays, place of work, health, and safety.
• Access to specific training activities exclusively provided within the framework of the LEAD AI programme.

*) Subject to membership in the Norwegian National Insurance Scheme. 

**) Right to paid parental leave requires 6 months paid work before first day of leave. See full requirements.

How to apply:

Before starting the online application process, please familiarise yourself carefully with our application requirements in the Guide for Applicants. It is essential that all required attachments (see next section) are uploaded via our electronic recruiting system JobbNorge. Before uploading any documents in the portal (to minimise repetition of information):

• In the ‘JobbNorge-application field’: Only write your name.
• In the ‘JobbNorge-CV form’: Only fill in your 1) personal details, 2) information about your PhD-degree (in the field ‘Academic qualifications’) and 3) recent relevant work experience.
• You do not need to fill in any other sections in the JobbNorge form, as all the information we need will be provided by you when attaching the mandatory elements listed in the next section.

Your application must include:

• Research proposal (5 pages, using a template based on the evaluation criteria), must be relevant to the AI-related research themes defined by LEAD AI host research groups in the call. It is essential that you discuss the details of the project idea with your potential UiB supervisor at an early stage in the application process, to ensure that there is a match between resources and expertise needed to implemement the project. 
• A motivation letter.
• A CV, including publication list, detailing the full scientific track record, relevant other achievements and career breaks (e.g., parental, and long-term sick leave, compulsory military service, and non-academic work)
• Mobility declaration with supporting documentation (e.g., employment contract, rental agreement. etc.).
• Table of potential ethics and security issues that might apply to the research proposal (self-assessment).
• An initial self-assessment of opportunities for mandatory and recommended open science practices.
• Scanned copy of the PhD diploma or documentation of formally delivered doctoral thesis (translated to English if necessary; Scandinavian languages are accepted).
• References. Letters of recommendation from the graduating university or previous employers are encouraged.

The application and appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge

General information:

Detailed information about the position can be obtained by contacting: 

For further details about the position, please contact: 

• Professor Kirsty Kitto, kirsty.kitto@uib.no 

For HR related questions contact:

• Head of Administration, Jorunn Viken, jorunn.viken@uib.no, tel: +47 55 58 32 41 

Diversity is a strength that enables us to solve our tasks even better. UiB therefore needs qualified employees regardless of gender, ethnicity, religion, worldview, disability, sexual orientation, gender identity, gender expression, and age.

We encourage men to apply. If multiple applicants have approximately equivalent qualifications, the rules pertaining to moderate gender quotas shall apply.

The University of Bergen applies the principle of public access to information when recruiting staff for academic positions.

Information about applicants may be made public even if the applicant has asked not to be named on the list of persons who have applied. The applicant must be notified if the request to be omitted is not met.

We encourage applicants with disabilities, immigrant backgrounds, or gaps in their CV to apply. By indicating such circumstances in your application, you may receive favourable consideration. We ensure that at least one qualified applicant from each of these groups is invited for an interview as part of our commitment to inclusivity and equal opportunity.Further information about our employment process can be found here.