Uppsala University, Department of Information Technology

The Department of Information Technology has a leading position in research and education. The Department currently has about 300 employees, including 120 teachers and 110 PhD students. More than 4000 students study one or more courses at the department each year.
More info can be found at the Department’s website.

At the Division of Systems and Control, we develop methodology for and applications of automatic control, system identification, and machine learning. Developing mathematical models that capture real-world dynamical phenomena evolving in and interacting with their environment is central to all these areas of information technology. Based on the models, algorithms are developed that allow machines and humans to operate efficiently in the world around us. Optimization methods are of central importance since they constitute the computational core of control, system identification, and machine learning. Model uncertainty quantification is an important aspect since it allows for design of algorithms with performance guarantees.

The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the Delft University of Technology, University of Cambridge, University of Oxford, Imperial College, University of British Columbia, University of Sydney, University of Newcastle and Aalto University.

Read more about our benefits and what it is like to work at Uppsala University

Duties/Project description:
The successful candidate will join the Secure Learning and Control Laboratory, a growing interdisciplinary research group doing basic and applied research at the intersection of cybersecurity, control theory, and machine learning. Our vision is to develop methodologies for designing intelligent autonomous decision-making systems that are secure and resilient against malicious adversaries and natural failures.

The postdoctoral position will intensify our work (on both method development and applications) in secure learning and control and may, if the candidate wishes, be paired with real-world applications in e.g. water networks, smart grids, electrical vehicles, or process industry. The position might also include teaching in related subjects (max 20%).

Project description: Secure Learning and Control

The rapid advances in computation technologies and increase in data bring new possibilities for embedding intelligence in cyber-physical systems and allowing them to safely interact with dynamic environments. Intelligent cyber-physical systems are achieved by the seamless integration of hardware, software, communication technologies, systems and control engineering, and machine learning. They have enabling applications in areas such as robotics and autonomous vehicles, industrial processes, or energy systems and other critical infrastructures. 

Despite their broad use and enabling applications, these systems are prone to failure due to external physical events that are often natural, but could also be due to malicious actions performed by adversaries on the digital components. The failure of cyber-physical systems can have devastating consequences that extend from the digital to the physical world.

Our research aims to create novel system-theoretic methodologies enabling the design of intelligent cyber-physical systems that are secure against adversaries and natural failures. The research scope is particularly focused on control theory, optimization, and machine learning.

The scope of the research to be conducted is the development of novel probabilistic risk metrics and optimization-based design methods for learning and control in closed-loop systems that jointly consider the impact and the detectability constraints of attacks, as well as a diverse set of adversary models with uncertainty.  Possible topics include, but are not limited to: investigating the impact and detectability of classes of attacks (e.g., delay, Denial-of-Service, or false data injection attacks); robust control and fault detection for increased security; analysis of data-driven control approaches from a security perspective; exploring connections and differences between adversarial training, robustness, and security in the context of machine learning for control.

The precise research scope will be decided in a dialog between the candidate and the supervisor after a successful appointment.

This position is part of the project “Secure and Resilient Control Systems” funded by a grant from the SSF Future Research Leaders Program. The project aim is to create novel methodologies addressing cybersecurity problems under uncertainty in learning and control systems.

More information is available via the link to the project website.

To qualify for employment as a postdoctoral fellow, you must have a PhD degree in in a field closely related to this position or a foreign degree equivalent to a PhD degree in in a field closely related to this position. The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are primarily considered for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

The applicant must have a strong background in method development and the use of control theory.
As a person, you are creative, thorough and have a structured approach. When selecting among the applicants we will assess their ability to independently drive their work forward, to collaborate with others, to have a professional approach and to analyze and work with complex problems. Great emphasis will be placed on personal characteristics and personal suitability. Excellent knowledge of oral and written English is a requirement.

Additional qualifications
Additionally, experience of interdisciplinary research is a merit. Experience and courses in one or more of the following subjects is valued: nonlinear control, system identification, robust control, estimation and fault detection, model predictive control, data-driven control, statistical machine learning, and optimization. For this project, we also value knowledge in security and privacy.

The application must contain:

  1. A curriculum vitae (CV);
  2. A copy of relevant degrees and grade documents (translated into Swedish or English);
  3. A list of publications;
  4. Up to five selected publications in electronic format;
  5. A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page). The statement should explain how your profile fits the position;
  6. Contact information for two references (name, e-mail, and phone number);
  7. A cover letter briefly describing your motivation for applying for this position and the earliest possible employment date (max 1 page).

About the employment
The employment is a temporary position of 2 years according to central collective agreement. Scope of employment 100 %. Starting date as agreed. Placement: Uppsala.

For further information about the position, please contact: Associate Professor André Teixeira (phone: +46  18-471 5414, email: andre.teixeira@it.uu.se).

Please submit your application by 17 April 2023, UFV-PA 2023/382.

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden.  

Type of employment Temporary position
Contract type Full time
First day of employment As agreed
Salary Fixed salary
Number of positions 1
Full-time equivalent 100%
City Uppsala
County Uppsala län
Country Sweden
Reference number UFV-PA 2023/382
Union representative
  • Seko Universitetsklubben, seko@uadm.uu.se
  • ST/TCO, tco@fackorg.uu.se
  • Saco-rådet, saco@uadm.uu.se
Published 15.Feb.2023
Last application date 17.Apr.2023 11:59 PM CEST

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