Postdoctoral fellow in Statistics - Hiring in process/Finished, not possible to apply

The Department of Statistics employs around 15 researchers, teachers, PhD students, and other staff. We conduct research within a number of areas, such as the analysis of high dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modelling, applied research in the social sciences, and stochastic models and computational statistics. More information can be found on the department's home page, https://stat.lu.se/en/research.

Lund University announces a limited-duration (two years) position as Postdoctoral Fellow in statistics. The position is full-time, with placement at the Department of Statistics, Lund University School of Economics and Management (LUSEM). The position is funded by the Vinnova project CLAIRE.

Climate-AI-infection-REsponse (CLAIRE)

The project aims to develop robust climate-based algorithms and decision-making frameworks to support public health adaptation to infectious disease risks following climate change. The decision-making frameworks developed and validated within the CLAIRE project will enable timely and coordinated public health responses to prevent climate-sensitive infectious disease emergence and outbreaks in Sweden and Europe at large. For this purpose, CLAIRE will make use of machine learning (ML) methods, such as variational autoencoders and Bayesian spatio temporal models, to provide new algorithms and prototype decision-making dashboards of value for public health protection, specifically tailored to the needs of the European Centre of Disease Prevention and Control (ECDC). The project is a collaboration between Umeå University, Lund University, The Swedish Meteorological and Hydrological Institute (SMHI), and the European Centre for Disease Pre-vention and Control (ECDC).

Subject description

The Department of Statistics at Lund University is seeking applicants for a postdoctoral position in the area of statistical methods and/or statistical machine learning.

Assigned duties

The main duty involved in the postdoctoral position is to conduct research within the scope of the CLAIRE project. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning.

The applicant is expected to conduct independent research in modern statistical methods and machine learning, within the scope of the Vinnova project CLAIRE. He or she is expected to collaborate with other researchers at the department, be willing to interact with students, and actively participate in the activities of the department. Production of publications and active participation in conferences is also expected.

In recent years the department has developed several courses within data science, e.g., Bayesian methods, Data Mining and Visualisation, Deep Learning, and AI methods. The applicant is expected to teach these types of courses. Teaching can be at the undergraduate, graduate, and doctoral levels. Supervision of undergraduate essays at the Bachelor and Master levels is also expected.

Qualification requirements

Appointment to this position requires that the applicant has a PhD, or an international degree equivalent to a PhD, within Statistics, Econometrics, Mathematical Statistics, Mathematics, Physics or an equivalent field, completed no more than three years before the last date for applications. Under special circumstances, the doctoral degree can have been completed earlier.

Assessment criteria and other qualifications

This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of applicants will primarily be based on their research qualifications and potential as researchers. Particular emphasis will be placed on research skills within the subject and how well the candidate will fit within the CLAIRE project.

Appointment to this position will use the following assessment criteria:

• A good ability to develop and conduct high-quality research. Especially focus on numerically efficient algorithms and Gaussian processes.
• Results and distinctions that attest to the applicant's ability to do research.
• Teaching skills.
• Ability to engage in activities that inform society at large about research.

Ability to apply existing ML methods to data relevant for the CLAIRE project as well as develop new ML methods is expected. The applicant will also be expected to have a broad knowledge of ML and statistics in general and numerically efficient methods for Gaussian processes, and stochastic transport modelling in specific.

The position includes teaching in English and therefore requires a good command of the English language.

Consideration will also be given to good collaborative skills, drive, and independence, and how the applicants´ experience and skills complement and strengthen on-going research within the department, and how they stand to contribute to its future development.

Terms of employment

This is a full-time, fixed-term employment of two years. The period of employment is determined in the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a postdoctoral fellow”) between the employer organization and the union organizations (http://www.arbetsgivarverket.se/avtal--skrifter/avtal/avtal-om-tidsbegransad-anstallning-for-adjungerad-larare2/).

Application procedure

Please use Lund University's job application portal when applying. Click on "Apply for position" in the announcement. For more information, please go to our web pages http://www.lunduniversity.lu.se/about/work-at-lund-university and https://www.lunduniversity.lu.se/about-lund-university/work-lund-university/applying-position.

The application must be written in English.

The application should contain:

• A personal letter in which the applicant gives a short description of him/herself and his/her research interests.
• Curriculum vitae.
• PhD diploma.
• Doctoral dissertation.
• Other documents that the applicant wishes to submit.
• References.