We are recruiting!
Experienced candidates as well as postdoc level candidates are of interest.
Up to three Positions as Associate Professor in Machine Learning
The positions will be formally announced over http://jobbnorge.no very soon.
For now, contact Robert (email@example.com), Michael (firstname.lastname@example.org) or Benjamin (email@example.com).
The Department of Physics and Technology has open up to three permanent positions as associate professor in the internationally recognized UiT Machine Learning Group to further strengthen UiT’s push and strategic initiatives within machine learning research. The positions are closely connected to Visual Intelligence, a Centre of Research-based Innovation hosted by the group, and a new Centre-of-Excellence called Integreat.
The positions will be part of the UiT Machine Learning Group. The Machine Learning Group is an internationally recognized research group spanning foundational and applied research in machine learning methodology, algorithms, and AI.
The faculty positions shall strengthen the UiT Machine Learning Group’s scientific excellence and high- profile in the Centre of Research-based Innovation SFI Visual Intelligence, which is headed by the group, and the new Centre of Excellence SFF Integreat – The Norwegian Centre for Knowledge-based Machine Learning (starting fall 2023).
The positions shall contribute to research at a high international level within the machine learning field. The positions shall leverage the foundation in machine learning research to pursue applications of high societal value, notably within health and medicine, and potential impact via collaborative research with industry and the public sector. The positions shall contribute to research-based innovations.
The faculty members will be important for the teaching of the courses under the responsibility of the UiT Machine Learning Group, and to contribute to the development of the study programs that the group are central parts of, such as the study programs in AI and in Applied Physics and Mathematics. The faculty members will engage in dissemination of research and outreach activities.
The faculty members that enter these positions shall thus contribute to the fostering of relevant machine learning and AI competence nationally and regionally via research, innovation, teaching, and dissemination.
Besides being funded over the Research Council of Norway’s (RCN) Centre of Research-Based Innovation and Centre of Excellence programs, the UiT Machine Learning Group has several ongoing projects such as RCN FRIPRO/IKTPLUSS/Industry-PhD, Horizon Europe, as well as projects funded over UiT’s strategic fund.
Your field of work
You fit well the profile we are seeking for the new faculty members if you have a strong desire to develop the next generation machine learning methodologies for extracting knowledge from ever increasing and challenging imaging data as well as other data types and sources to gain insight, to create value, and solve real world applications of value to humankind and towards the UN sustainability goals.
Within SFI Visual Intelligence and the UiT Machine Learning Group as a whole, a dominating innovation area is within health and medicine. Our research contributes to diagnosis support and decision support specifically by extracting patient-specific information from electronic health records and by medical computer vision for important tasks such as cancer characterization. You can help transform healthcare for the future needs, with AI as an integral part. You will collaborate with clinicians in this endeavour and to help people through research. At least one of the positions will have an emphasis on novel machine learning methodology development for such health applications.
Other innovation areas include the marine sciences for abundance estimation and sustainable harvest of the oceans, as well as environmental monitoring. Through machine learning, we can aid the climate, the oceans and the Earth. As a faculty member in the UiT Machine Learning Group, you will have the chance to make impact on the world and to help shape the future of society by the transformational power of AI for better machine learning solutions that are trustworthy and ethically sound.
The UiT Machine Learning Group is internationally leading in deep learning and neural networks research, where key research challenges are learning from limited data, interpretability and XAI, uncertainty quantification, and the integration of prior knowledge and context into the solutions. Dominant current directions in the group include e.g. self-supervised learning, unsupervised learning, representation learning, graph-based learning, information theoretic learning, and knowledge-based learning.
You will strengthen the group’s research portfolio within cutting edge machine learning research as described above and beyond. You are expected to seek opportunities to collaborate with the current faculty members in the group. In particular, you are expected to become key scientists in the Visual Intelligence and the Integreat research centres. The UiT Machine Learning Group’s strength lies very much in teamwork and joint supervision of students.
You are expected to actively seek external funding (regionally, nationally, and internationally) and to build a project portfolio and to establish a research network. You are expected to contribute to the dissemination of the group/centre’s research results, outreach, and in the organization of social events, seminars and workshops such as the group’s Northern Lights Deep Learning Conference http://nldl.org.
You will participate in the teaching of machine learning courses and possibly in courses in related topics such as signal and image processing to students from undergraduate to postgraduate levels, including providing supervision of master and doctoral thesis projects. Commitment to internationally high standards for teaching quality, recruitment and public outreach activities is expected. Teaching duties will be connected to the established study programs related to machine learning and AI at UiT.
You must hold a PhD in machine learning or related relevant fields and you must document experience in executing independent original research.
You must have a strong background in machine learning methodology research as documented in publications at a high level within machine learning journals and conferences. Publications in quality venues such as IEEE TPAMI, IEEE NNLS, IJCAI, AAAI, ICML, NeurIPS, ICLR, CVPR, ECCV, ICCV, AISTATS, etc, will be given consideration.
In addition, you must have a strong interest in, and preferably experience with, development of novel machine learning methodology for relevant applications, also documented in publications at a high international level (e.g. in venues such as Medical Image Analysis, IEEE Transactions on Health and Biomedical Informatics, Transactions on Geoscience and Remote Sensing, etc). A blend of research on machine learning methodology development and the leveraging of such developments in important application domains are qualifications we are looking for.
You are developing cutting edge and modern machine learning methodology, e. g. with research focus in areas such as deep learning (neural networks), probabilistic learning, geometric learning, knowledge-driven learning and information theoretic learning, or combinations thereof. You being active in deep learning research, including image analysis, makes you particularly attractive in the context of Visual Intelligence, given the heavy focus in that direction in this centre, and such experience will be given emphasis in the evaluation.
You should be creative and must be able to take on and develop own initiatives. Consideration will be given to innovation activities and to communication experience for the dissemination of science.
We aim to increase diversity within the machine learning field and encourage female candidates and candidates that are traditionally underrepresented within the field to apply. Being part of our vibrant research centres and group gives huge opportunities for working together and for being stronger together. Experience in team work and interdisciplinary work will be a plus.
Documented external funding, experience with research leadership and relevant collaboration with industry for innovation activities will be rated positively. As an associate professor, we expect you to aim at developing yourself further to a full professor.
UiT follows national guidelines for professorial promotion within Mathematics, Science and Technology disciplines when evaluating candidates for professorships.
The future potential for publishing in prestigious journals and conference proceedings in machine learning and related fields will be given high emphasis. The publishing record will therefore be assessed with respect to the stage of career of the candidate.
We will emphasize personal suitability in our assessment. We expect you to actively contribute to academic culture, think beyond the core of your own research interests and have good collaboration skills necessary for joint interdisciplinary projects. You must be willing to participate actively in the ongoing development of the discipline, the department, and the university as a whole.
For permanent positions, you must document teaching qualifications by submitting a teaching portfolio, see the website for basic pedagogical competence. If such a teaching portfolio is lacking, you could be eligible for an interim appointment (see below).
You must be fluent in oral and written English. Applicants who are not fluent in a Scandinavian language must learn Norwegian within 3 years and pass the language exam level C1 (“Bergenstesten” or equivalent).
Letter of application.
CV including information relevant for the qualifications and a full list of publications with bibliographical references.
Brief research plan and vision statement (1 page) for the next 3-5 years, also identifying internal and external collaboration partners. The research plan must contain considerations on how the candidate envision to contribute to Visual Intelligence and the to the UiT Machine Learning Group in general.
Documentation of external research funding raised during the career. This documentation should be clear about who had what type of roles in the projects.
Three references with contact information.
Up to ten top scientific publications. Your doctoral thesis is regarded as one work.
Description of your research stating which works you consider most important and a brief description of the other listed works.
Teaching portfolio of minimum three pages, informing about your work with students. Describe and reflect on your own teaching, and present contributions to development of teaching. It will typically contain teaching philosophy, documentation of teaching activities demonstrating planning, accomplishments and assessment, evaluations of the teaching, and experiences in developing courses and curricula. Attach certificates, reports, and other relevant documents.
Form for teaching qualifications (if you lack a teaching portfolio - for interim appointments, see further below).
Documentation has to be in English or a Scandinavian language. When the positions are formally announced, you will submit your application electronically through Jobbnorge. For enquiries about for the position, please contact:
Director of Visual Intelligence and Co-Director of Integreat, Professor Robert Jenssen: firstname.lastname@example.org
Head of Machine Learning Group, Associate Professor Michael Kampffmeyer: email@example.com
Associate Professor Benjamin Ricaud: firstname.lastname@example.org
If there are no fully qualified applicants for the position, we may make an interim appointment to qualify for a period of three years. Before the three-year period elapses, a permanent appointment is made in the event that you are suitably qualified, based on a new assessment.
In the event of an interim appointment based on lacking teaching qualifications, you must develop an approved teaching portfolio. The appointment will become permanent if you are found to be suitably qualified before the three-year period elapses.
• The possibility to work in a vibrant group at the forefront of machine learning research • R&D sabbatical conditions that are possibly the best in Norway
• A good working environment
• Good welfare arrangements for employees
• Good arrangements for pension, insurance and loans in the Norwegian Public Service Pension Fund
The remuneration for Associate Professor is in accordance with code 1011. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund is deducted. In addition, UiT pays approx. 12 % directly to the Pension Fund on top of the salary.
Employees in permanent positions as professor/associate professor have the right to apply for a paid sabbatical (research and development).
In general, an associate professor spend an equal amount of time on teaching and research and development work, after time spent on other duties has been deducted. As a norm, the time resources spent on administrative duties constitutes 5 % for academic staff in this category of position. The allocation of working hours is flexible and allocated on a case-by-case basis.
More information about moving to Tromsø: http://uit.no/mobility.
We make the appointment in accordance with the regulations in force concerning State Employees and Civil Servants, and guidelines at UiT. At our website, you will find more information for applicants. UiT The Arctic University of Norway wishes to increase the proportion of females in senior research positions. In the event that two or more applicants are found to be approximately equally qualified, female applicants will be given priority. UiT The Arctic University of Norway has HR policy objectives that emphasize diversity, and encourages all qualified applicants to apply regardless of their age, gender, functional ability and national or ethnic background. The university is an IW (Inclusive Workplace) enterprise, and emphasize making the necessary adaptations to the working conditions for employees with reduced functional ability.