Up to two postdocs in computational statistics for machine learning
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 44.000 students, 7.100 employees and a turnover of SEK 7 billion.
The Department of Information Technology has a leading position in both research and education at all levels. The department currently has about 280 employees, including 120 teachers and 110 graduate students. Each year more than 4000 students take one or more courses at the department and c. 30 research groups are attached to it. More information: http://www.it.uu.se.
The Department of Information Technology consists of five divisions. The research at the Division of Systems and Control is ranging from Signal Processing, Automatic Control, System Identification and Statistical Machine Learning. The division is currently expanding in several research areas, including machine learning.
Read more at Systems and Control
Duties: A successful applicant will conduct research in the areas of computational statistics and machine learning. Specifically, the position involves methodological development of new probabilistic models and new inference methods in order to address some of the shortcomings of the current state-of-the-art. This may include:
(1) Developing efficient Bayesian inference algorithms for large-scale latent variable models in data rich scenarios.
(2) Finding ways of systematically combining different inference techniques, such as variational inference, sequential Monte Carlo, and deep inference networks, resulting in new methodology that can reap the benefits of these different approaches.
(3) Developing efficient black-box inference algorithms specifically targeted at inference in probabilistic programs. This line of research may include implementation of the new methods in the probabilistic programming language Birch, currently under development at the department.
In addition to methodological development, the position involves performing a theoretical analysis of the developed methods to ensure that they rest on a solid foundation and to provide better insights into their properties. Part of the assignment is furthermore dedicated to contributing to the scientific discussion and development at the department, e.g. by participating in reading groups and PhD supervision. Depending on the candidate's career interest the position may also include up to 20 % teaching (in English or Swedish).
Qualifications: To qualify for an employment to this post-doctoral position the applicant must hold a PhD degree or a foreign degree equivalent to a PhD degree in statistics, machine learning, signal processing, or in a close research field which by the employer is regarded as relevant for the research subject. The PhD degree must have been obtained no more than three years prior to the application deadline. The three year period can be extended on circumstances such as sick leave, parental leave, duties in trade unions, etc.
A successful candidate should have a documented experience/competence in applied mathematics, statistics and/or machine learning. A track record in computational statistical inference methods (sequential Monte Carlo, Hamiltonian Monte Carlo, variational inference, stochastic optimization, etc.) is highly desirable. The applicant should have a documented skill in prototyping new inference methodology in a suitable software environment. The applicant should furthermore have a strong drive towards performing fundamental research; a documented capability to perform research on an internationally recognized level, supported by a publication record; the ability and interest to work in a team; and strong communication skills.
Additional qualifications: Software development skills will be seen favorably but are not essential.
Application: The application should include a statement (at most 2 pages) of the applicant’s motivation for applying for this position, including the candidate’s qualifications and research interests, a description of the PhD thesis, and evidence of self-motivation and constructive teamwork. The application should also include a CV; degrees and grades (translated to English or Swedish); a copy of, or a web reference, to the PhD thesis; a complete list of publications, with up to five important publications for the position highlighted including a brief description of the main results of these publications and a description of the candidate’s own contributions; other relevant documents; and a specification of the earliest possible starting date. References with contact information and up to two letters of recommendation may be provided.
Uppsala University strives to be an inclusive workplace that promotes equal opportunities and attracts qualified candidates who can contribute to the University’s excellence and diversity. We welcome applications from all sections of the community and from people of all backgrounds.
Pay: Individual salary
Starting: As soon as possible or as otherwise agreed.
Type of employment: Temporary position for two years.
Working hours: 100 %
For further information about the position please contact
Fredrik Lindsten, email@example.com.
You are welcome to submit your application no later than 2017-11-17, UFV-PA 2017/3431.
Are you considering moving to Sweden to work at Uppsala University? If so, you will find much information about working and living in Sweden at www.uu.se/joinus. You are also welcome to contact the International Faculty and Staff Services at firstname.lastname@example.org.
Please do not send offers of recruitment or advertising services. Applications must be submitted as described in this advertisement.
Placement: Department of Information Technology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed pay
Number of positions: 2
Working hours: 100%
County: Uppsala län
Ellena Papaioannou, Seko
Per Sundman, Saco-rådet 018-471 1485
Suzanne Borén Andersson, TCO/ST 018-471 6251
Number of reference: UFV-PA 2017/3431
Last application date: 2017-11-17
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