WASP AI Assistant Professor in Statistical Learning - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

We invite applications for a tenure-track position in statistical learning. We especially welcome applicants who work in non-convex inference and learning. The position comes with a large recruitment package including one PhD student, two postdocs, and discretionary funds of 60 000 SEK yearly. Apply before 2021-07-02.

Background

The expansion of Artificial Intelligence (AI) is one of the most exciting developments of the 21st century. The centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) has a special program for developping the mathematics of AI and strengthen Sweden's position in the field. The current call is part of the WASP recruitment program and is intended to further develop the research environment within statistical learning at the Department of Mathematics and Mathematical Statistics, Umeå University.

 Industrial robots, autonomous cars, stocks trading algorithms, and deep network assisted evaluation of medical images all crucially involve real-time, intelligent and automated decision making from complex and heterogeneous data, at ever growing scale and pace. This presents unprecedented theoretical and algorithmic challenges and opportunities for researchers in collecting and transforming data into information, predictions and intelligent decisions.

Optimization theory is vital to modern statistical learning and at the forefront of these advances, and the main objective of this position is to develop the next generation of learning theory to address the above challenges in the context of modern statistical learning, and potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data.

Subject description

The subject area is mathematical statistics with specialisation in statistical learning. We especially seek candidates who wish to develop nonconvex inference and learning. The proposed research field has a focus on developing new mathematical, statistical and computational methodology for nonconvex learning, establish the theoretical framework, and explore their applications in AI

Description of the employment

The applicant should contribute to the build-up of the research environment on mathematics for AI, with focus on statistical learning, and supervise PhD students and postdocs. The description of the applicant's intended research will be given great significance in the evaluation process.

You are expected to work independently and your main focus should be to develop novel theory in statistical learning, preferably focusing on nonconvex inference and learning for AI. Education at the department will offer an opportunity to supervise qualified degree projects in collaboration with external partners. You are eventually expected to create a new research group, while engaging in WASP's graduate school, WASP national meetings and other activities. You are also expected to actively engage in applying for external research funding.

The employment as Assistant Professor (biträdande universitetslektor in Swedish) is a five year position where 80% of the time is devoted to research. At the end of this employment period, an Assistant Professor has the right, after requesting, to be examined for a promotion to Associate Professor at Umeå University.

Eligibility requirements

According to Chapter 4, Section 4 of the Higher Education Ordinance: A person qualified for appointment as an Assistant professor is a person who has been awarded a PhD or has the corresponding research expertise. Primary consideration should be given to a person who has been awarded a PhD or achieved the equivalent expertise within five years prior to the deadline for application for employment as an Assistant professor. However, a person who has been awarded a PhD or achieved the equivalent expertise at an earlier date may also be considered if there are exceptional circumstances. Exceptional circumstances are sick leave, parental leave or other similar circumstances. The subject for the doctoral degree should be Mathematics, Mathematical Statistics, or a closely related subject.

The applicant must have international research experience and documented knowledge that strengthens the competence in the research field in Sweden. The applicant must also be able to demonstrate an ability to work, collaborate, and communicate in a responsive and independent way, in contexts both inside and outside of academia. The applicant must be fluent in oral and written English.

The applicant must have contributed to the theory of statistical learning.

Assessment criteria and their weight

In the selection of candidates, the degree of research expertise shall be decisive. In addition, the degree of educational expertise as well as administrative and other competences of importance shall be taken into account with regard to the subject content of the employment and the duties to be included in the employment. Moreover, the degree of skill for development and leading activities and personnel at the university as well as the ability to cooperate and interact with the surrounding society and inform about research and development work will be considered.

Scientific expertise

Scientific expertise shall be demonstrated through independent scientific productivity. The criteria for assessment are:

• Breadth and depth of research – quality and extent
• Originality in research
• Productivity
• Contributions to the international research community
• Commissions of trust within the research community
• Ability to competitively obtain research funding
• Collaboration with the surrounding community

The applicant should preferably have made contributions in nonconvex inference and learning theory and methodology development. Achievements of new theory in both optimisation front and statistical learning front with applications in deep neural networks and inference are of particular interest for the position.

Experience of successful development of mathematical and statistical methods for analysis of big data and experience of interdisciplinary research projects will be meritorious, as will a documented ability to attract competitive research funding.

Pedagogical expertise

The criteria for evaluation of pedagogical expertise are:

• ability to plan, implement and evaluate teaching and an ability to supervise and examine students at every level of education
• ability to vary teaching methods and examination forms in relation to anticipated study results and the nature of the subject
• experience of collaboration with the surrounding society in planning and implementation of education
• participation in the development of learning environments, teaching aids and study resources
• a reflective approach to student learning and one’s own role as a teacher

Pedagogical expertise can be attained through education in university pedagogy, other education of relevance for teaching at a university level or documented and proven experience teaching at the university level.

Other criteria

The ability to independently organise, lead and manage projects will be considered.

Application

The application should preferably be written in English, and include the following items:

1. A personal cover letter, including contact information
2. Certified copies of relevant university degrees and diplomas
3. Copies of five research publications relevant to the position, numbered according to the publication list
4. Attachments to a pedagogical portfolio (see p. 3.6 in Umu Application form)
5. Certificates or documents from appraisals from e.g. the Head of Department, Director of Studies, colleagues, external assessors and students.

6. CV/Umu Application form

   Fill in the application form (word). Submit it attached to your application in the Varbi system as your CV. Following information must be provided in the application form:

• basic information that includes academic and professional qualifications
• a list of articles published
• an account of research experience
• an account of pedagogical experience and activities
• 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 (maximum 5 pages)
• references, including contact details

See instructions for what to include in the application, as well as how to describe scientific and educational activities (pdf)

Applications should be made via e-recruitment system Varbi and be received no later than 2021-07-02. 

Contact information

For further information, contact Professor Jun Yu, 090-786 51 27, jun.yu@umu.se, and/or Head of Department, Åke Brännström, 090-786 78 62, ake.brannstrom@umu.se.

Applying for promotion to Associate professor

As assistant professor, upon request, can be promoted to Associate professor if he or she meets the requirements for employment as an Associate professor and upon review is determined to meet the qualifications for such employment according to the evaluation criteria that shall be applied for promotion to Associate professor. This promotion entails a permanent position as Associate professor.

The application for promotion shall be submitted six months before the fixed-term appointment ends. If an Assistant professor after evaluation is not promoted, the fixed-term appointment ends.

Established criteria for promotion can be found in the employment profile decided by the dean of the Faculty of Science and Technology on May 11, 2021.

Umeå University is one of Sweden’s largest institutions of higher education with over 34,000 students and 4,000 faculty and staff. We are characterised by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison. Umeå University is also the site of the pioneering discovery of the CRISPR-Cas9 genetic scissors - a revolution in genetic engineering that has been awarded the Nobel Prize in Chemistry.

At Umeå University, everything is nearby. Our cohesive campus environment makes it easy to meet, collaborate and exchange knowledge, which promotes a dynamic and open culture where we rejoice in each other's successes.