PhD student in Automatic Control focusing on Secure Learning and Control - Hiring in process/Finished, not possible to apply

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 a 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 the design of algorithms with performance guarantees.

The Division of Systems and Control enjoys a wide network of strong international (worldwide) collaborators. 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 Ph.D. students to get a solid international experience during their Ph.D.

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

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.

This position is part of the project “Secure and Resilient Control Systems” funded by a grant from the SSF Future Research Leaders Program. The successful candidate will join the research group 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.

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

Duties
A Ph.D. student is expected to devote his/her/their time to graduate education mainly. The rest of the duties involve teaching at the Department, including also some administration, to at most 20%.

Requirements
A Ph.D. position at the Division of Systems and Control requires the following:

• a completed (or near to completing) Master of Science, or equivalent, in a field that is relevant to the topic of the project,
• good communication skills with sufficient proficiency in oral and written English, as well as excellent study results,
• personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving are essential.

Additional specific requirements are as follows: (i) proficiency in programming (preferably in Matlab or Python), and (ii) knowledge of control theory background or applied mathematics with a focus on linear algebra, statistics, and optimization.

Additional qualifications
Experience and courses in one or more subjects are valued: control theory, linear systems, nonlinear control, robust control, estimation, automatic control, model predictive control, data-driven control, statistical machine learning, and optimization.

Application
The application must include: 1) a statement (at most 2 pages) of the applicant’s motivation for applying for this position, including a self-assessment on why would you be the right candidate for this position; 2) a CV; 3) degrees and grades (translated to English or Swedish); 4) the Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents; and 5) references with contact information (names, emails and telephone number) and (if possible) up to two letters of recommendation.

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 possible 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.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. 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 28 February 2023, UFV-PA 2023/332.

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