Assistant Professor in Scientific Computing with specialisation in data- driven life science

Uppsala University is a comprehensive research-intensive university with a strong international standing. Our mission is to pursue top-quality research and education and to interact constructively with 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 46.000 students, 7.300 employees and a turnover of SEK 7.3 billion.

The Department of Information Technology has a leading position in research and all levels of higher education. Today the department has 280 employees, including 120 academic staff and 110 full-time PhD students. The Department comprises research and education in a spectrum of areas within Computer Science, Information Technology and Scientific Computing. More than 4000 students take one or several courses offered by the Department each year.

The position is hosted by the Division of Scientific Computing within the Department of Information Technology. As one of the world’s largest focused research environments in Scientific Computing the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software development and high-performance computing. The division is currently in an expansive phase in new emerging areas such as cloud and fog computing, data science, and artificial intelligence, where it plays key roles in several new strategic initiatives at the University. The research activities within this position will be hosted within the research program Computational Science in which several research groups work on the intersection of computer science, scientific computing and life-science.

This assistant professor position is part of the SciLifeLab Fellows program,
https://scilifelab-2021.accomplice-dev.se/research/fellows/, and includes a generous startup package that provides the candidate the opportunity to supervise PhD students. The position is one of several new strategic recruitments by SciLiefLab in the area. SciLifeLab (www.scilifelab.se), is a Swedish national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a joint effort between Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University. SciLifeLab was established in 2010 and appointed a national center in 2013. SciLifeLab coordinates a highly collaborative research community including more than 150 research groups working in the entire area of life sciences, such as molecular biology, cancer, immunology, stem cells and new biofuels. The technologies and services within our national infrastructure is used and developed in a symbiotic fashion with researchers within this community.

The Fellow will be given the opportunity to spend time at Navet, SciLifeLab’s hub, and at the Department of Cell and Molecular Biology (https://icm.uu.se/), to facilitate collaboration with biomedical research groups and with NBIS (National Bioinformatics Infrastructure Sweden; https://nbis.se).

Description of subject area of the employment: One of SciLifeLab´s strategic objectives for 2030 (https://scilifelab-2021.accomplice-dev.se/roadmap-2020-2030 ) is to build new research programs on data-driven life science (DDLS). This position should contribute towards that goal by establishing a foundation of strong research in the scientific computing aspects of the data-centric model. Research topics of particular interest include:

Data-centric computing enabling scalable analysis of very large amounts of scientific data generated by modern experimental platforms.
Modelling of quality and missingness/sparsity in raw experimental data, and methods for analysis resilient to issues of quality and non-random sparsity.
Privacy-preserving analysis of sensitive biological and medical data.
Scalable analysis over data streams.
Large-scale distributed machine learning.
Intelligent and autonomous management of e-infrastructure and experimental infrastructure supporting data-centric workflows.

It is expected that the Fellow has a demonstrated experience of working with applications in life-science. Of particular interest are combinations of data from multiple large-scale experimental platforms, so called multi-omics, and applications that meet the challenges of analysing sensitive biomedical data.

Duties:

Teaching, research and administration. Teaching duties, which normally amount to 20% on an annual basis, include course responsibility and course administration and supervision of second- and third-cycle students.
Follow developments within the subject area and the development of society in general that is important for the work at the university.
Establish a research group bridging data-intensive scientific computing and life science.

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 fulfils the criteria for promotion established by the Faculty Board he/she shall be promoted to and employed as Associate Professor.

Qualifications Required:

PhD in scientific computing, computer science, bioinformatics, or a field relevant to the description of the position, alternatively have the corresponding research competence or some other professional expertise. 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. The holder is expected to be able to teach in Swedish within two years.
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 expertise.

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. In assessing research expertise special weight will be attached to research merits in data-intensive scientific computing and data-driven life science.

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 scientific computing, data science, and data engineering.

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.

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.

Read more and apply

Further information

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.

University appointment regulations
Faculty appointment regulations
Instructions for application

For further information about the position, please contact Emanuel Rubensson, Head of the Division of Scientific Computing, emanuel.rubensson@it.uu.se or Elisabeth Larsson, Head of the Computational Science research program, elisabeth.larsson@it.uu.se.

Salary: Individually negotiated salary.

Starting date: As soon as possible or as otherwise agreed

Type of employment: Temporary full-time position six years.

Scope of employment: 100%

Please submit your application by December 11 2020, UFV-PA 2020/3319.

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%

Town: Uppsala

County: Uppsala län

Country: Sweden

Union representative: Seko Universitetsklubben seko@uadm.uu.se
ST/TCO tco@fackorg.uu.se
Saco-rådet saco@uadm.uu.se

Number of reference: UFV-PA 2020/3319

Last application date: 2020-12-11

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