Uppsala University, The Department of Information Technology

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 education. The Department currently has about 280 employees, including 120 teachers and 110 PhD students. More than 4000 students study one or more courses at the department each year. More info: http://www.it.uu.se/?lang=en

At the Division of Systems and Control we develop methodology and concrete tools for learning, reasoning and acting based on measured data. One of the cornerstones in our research is a probabilistic model allowing us to systematically represent and cope with the uncertainty that is inherent in most data. An important goal is to develop flexible models that can capture complex dynamical phenomena and their environments allowing machines and humans to better understand the world around us. The data and the model are two of the cornerstones of our research. The third cornerstone is the learning algorithm, with the fundamental objective of automatically constructing models based on data. It remains a major challenge to develop efficient and accurate learning algorithms capable of handling high-dimensional models, data rich applications, complex model structures, and diverse data sources that arise in many of the data analysis problems that we are currently facing. The fourth and final cornerstone of our research is that of control. The main task here is to make use of all that has been learnt from the data and represented within the probabilistic model to automatically make decisions and influence the current situation in a suitable manner. Information about our research is available from this popular scientific description  http://www.teknat.uu.se/news/nyhetsdetaljsida/?id=9994&area=5,16,17,50&typ=artikel&lang=en

The Division of Systems and Control has a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, Imperial College, University of British Columbia, University of Sydney, University of Newcastle and Aalto University. We strive for all PhD students to get a solid international experience during their PhD.

Project description:
The research projects for the advertised positions will be within the areas of machine learning, including the development and analysis of models or computational learning methods or reinforcement learning. Three concrete examples of potential research topics are briefly outlined below. As an applicant you are not required to specify a specific research topic in your application (but you are of course most welcome to do so if you want). Indeed, the topics below are provided mainly to make the advertised positions more concrete. We do welcome own initiatives and the precise research topic of each PhD student will be decided in a dialog between the student and the supervisor after a successful appointment. The three problem formulations that are most relevant for this opening are the following:

  1. Machine learning methods for causal inference and analysis: Modern machine learning (ML) methods are capable of learning accurate predictive models using data from real-world processes. However, they are in general ill equipped to predict outcomes under different conditions than those in which the data was collected. This limits the ability of conventional ML methods to learn the impact of decisions from data. To overcome such limitations, our interest is to develop new ML methods that can learn models which take into account the causal structure of processes and thereby predict outcomes under counterfactual conditions. This capability enables the assessment of the effect of decisions and intermediate factors on the outcomes for use in medical analysis or policy evaluation.

    This project is funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), which is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.

    The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. For more information about WASP, please see: https://wasp-sweden.org/ and https://wasp-sweden.org/graduate-school/

  2. Data-driven control and reinforcement learning: The goal of control engineering is to design controllers that make dynamical systems behave in a desired manner. Control theory plays an essential role in a wide range of applications, from simple consumer devices to industrial machines, autonomous vehicles and spacecrafts. Reinforcement learning (RL) is a subfield of machine learning that studies how data observed from a system can be used to enhance the performance over time. Recently RL has gained a lot of interest due to its demonstrable success in learning how to defeat the best human players in a range of well-known games.

    While control theory and RL share very similar goals, research in both fields have traditionally not overlapped very much. Our group has expertise in dynamical systems, control theory and machine learning. This is a good mix for research in the interface between RL and control theory, and one research direction of interest is the data-driven approach to control of non-linear dynamical systems. That is, design controllers that learn from observed data in order to improve the behaviour of a system. Here ideas from adaptive and optimal control as well as system identification and RL can be used in new and exciting ways.

  3. Developing deep dynamical models: Many real-world systems are dynamical processes. By learning models of such systems we obtain insights about their dynamics, get the capability to predict their behavior under different scenarios, and, in certain instances, control their output. Recently, promising progress has been made when it comes to developing deep dynamical models based on the so-called variational autoencoder. We foresee a lot more work to be done in this flourishing area where the expertise in our group in the fields of dynamical modeling and machine learning gives us a very good starting position. Possible applications exist for example within robotics, computer vision and experimental physics.

Duties: The position is for a maximum of five years and includes departmental duties at a level of at most 20 % (typically teaching).

Requirements: A PhD position at the Division requires a Master of Science or equivalent in a field that is relevant to the topic of the PhD thesis, good communication skills and excellent study results, as well as sufficient proficiency in oral and written English.

Additional qualifications: Experience in machine learning or computational statistics is valued.

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 and evidence of self-motivation and constructive teamwork. The application should also include a CV; degrees and grades (translated to English or Swedish); the Master’s thesis (or a draft thereof, and/or some other self-produced technical text), publications, and other relevant documents. References with contact information and up to two letters of recommendation may be provided. Applications may be submitted by candidates that have not fully completed the Master of Science degree (or equivalent), however all applicants should state the earliest feasible starting date of employment.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines http://regler.uu.se/?languageId=1.

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.

Salary: According to local agreement for PhD students.
Starting date: As soon as possible or as otherwise agreed.

Type of employment: Temporary position according to the Higher Education Ordinance chapter 5 § 7.

Scope of employment: 100 %

For further information about the position please contact: see http://www.it.uu.se/ (the department) or contact: Prof. Thomas Schön  (thomas.schon@it.uu.se), Dr Dave Zachariah (dave.zachariah@it.uu.se), Dr Niklas Wahlstöm (niklas.wahlstrom@it.uu.se) or Dr Per Mattsson (per.mattsson@it.uu.se).

Please submit your application by March 27, 2020, UFV-PA 2020/670.

Are you considering moving to Sweden to work at Uppsala University? If so, you will find a lot of information about working and living in Sweden at http://www.uu.se/joinus. You are also welcome to contact International Faculty and Staff Services at ifss@uadm.uu.se.

Type of employment Temporary position longer than 6 months
Contract type Full time
First day of employment As soon as possible
Salary Fixed salary
Number of positions 3
Working hours 100 %
City Uppsala
County Uppsala län
Country Sweden
Reference number UFV-PA 2020/670
Union representative
  • Seko Universitetsklubben, seko@uadm.uu.se
  • ST/TCO, tco@fackorg.uu.se
  • Saco-rådet, saco@uadm.uu.se
Published 21.Feb.2020
Last application date 27.Mar.2020 11:59 PM CET

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