Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in 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 over 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.
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 information 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
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.
These postdoctoral positions 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, or robotics. The position might also include teaching in related subjects.
Two concrete potential research projects are summarized below. As an applicant, you are encouraged to specify your preferred research project in your application to aid in the recruitment process, but this selection is not binding.
1st project: Secure Learning and Control Systems
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.
A core element of this research is the development of novel probabilistic risk metrics and optimization-based design methods that jointly consider the impact and the detectability constraints of attacks, as well as model uncertainty and prior beliefs on the adversary model. By combining relevant methodologies from control theory, reinforcement learning, optimization, and game-theory, the project will drive further the research frontier within secure control systems and adversarial learning.
More information can be found at the project’s website.
Project 2: Resilience in Large-Scale Critical Infrastructures
This position is part of the project “Resilience, Safety, and Security in Tree-structured Civil Networks” funded by a grant from the Swedish Research Council. Civil infrastructure networks critically support modern society by distributing resources and protecting communities from hazards. The resilience of civil infrastructure networks to disruptive events is the overarching topic of the project.
The main purpose of the project is to devise a coherent system-theoretical platform for resilience analysis of civil infrastructure networks, as well as resilience-informed control of those, under disruptive events such as failures, breakdowns, natural hazards, and cyberattacks .
The project will be conducted along three main threads: Mathematical modeling of civil infrastructure networks under disruptive events; Model-based assessment of resilience via operational indices; Resilience-informed control of the networks. The feasibility and efficacy of the developed mathematical models and algorithms is expected to be evaluated with respect to urban water and wastewater networks.
More information can be found at the project’s website
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.
Additionally, experience of interdisciplinary research is a merit. Experience in the following subjects is valued: security and privacy, statistical theory or machine learning, optimization. For the second project, we also value knowledge in dynamical and control systems and large-scale dynamical systems, as well as experience with scalable algorithms in the context of analysis and fault detection in large-scale systems.
The application must contain:
- A curriculum vitae (CV);
- A copy of relevant degrees and grade documents (translated into Swedish or English);
- A list of publications;
- Up to five selected publications in electronic format;
- A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page). Please indicate your preferred project and explain how your profile fits your selection;
- Contact information for two references;
- 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: firstname.lastname@example.org) and Assistant Professor Per Mattsson (phone: +46 18-471 3168, email: email@example.com).
Please submit your application by 29 July 2022, UFV-PA 2022/1871.
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Please do not send offers of recruitment or advertising services.
Submit your application through Uppsala University’s recruitment system.
Deadline: 2022-07-29 at 23:59
Unit: Department of Information Technology
Read the job description at the university homepage or apply.
Post expires on Friday July 29th, 2022