PhD student in Automatic Control focusing on probabilistic methods for secure control - Hiring in process/Finished, not possible to apply

Are you interested in working with control theory and probabilistic methods for securing intelligent cyber-physical systems, with the support of competent and friendly colleagues in a leading international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a doctoral student position at Uppsala University. 

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the department of Information Technology website.

At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security.

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 (Australia), and Aalto University. We strive for all Ph.D. students to get a solid international experience during their Ph.D.

Project description
The project aims to develop theory and probabilistic methods for securing cyber-physical systems (CPSs) possessing learning and control capabilities: digital devices that learn from data they collect, and automatically decide how to best interact with the physical world over time. 

Probabilistic methods are a natural way to deal with the uncertainty of physical systems and unintentional failures. However, attacks are not only probabilistic but can have strategic behaviors, tailored to causing harm while avoiding detection. Hence, it is critical to develop systematic approaches that integrate the probabilistic and strategic aspects of attacks in security analysis.

Duties
The successful candidate will work with probabilistic control methods in the context of model-free and model-based control under strategic adversaries. In particular, the doctoral student is expected to leverage control-theoretic and statistical methods to rigorously analyze and design the security of CPSs that learn how to control from data and prior models.

The successful candidate will join the research group Secure Learning and Control Laboratory at Uppsala University, 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.

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

Requirements
To meet the entry requirements for doctoral studies, you must

• hold a Master’s (second-cycle) degree in engineering physics, electrical engineering, applied mathematics, computer science, or in a similar field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

We are looking for candidates with

• a strong interest in control theory and probabilistic methods / statistical learning,
• good communication skills with sufficient proficiency in oral and written English,
• 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) excellent knowledge of control theory; (ii) proficiency in programming (preferably in Matlab or Python).

Additional qualifications
Experience and courses in one or more subjects are valued: control theory, linear systems, nonlinear control, robust control, statistical learning and optimization, system identification, and game theory.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.

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 you would be the right candidate for this position;
2. a CV;
3. degrees and transcript of records with 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;
5. references with contact information (names, emails and telephone number) and (if possible) up to two letters of recommendation.

Applicants who meet at least one of the entry requirements are strongly encouraged to apply. All applicants should state their earliest possible starting date.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date 15 August 2024 or 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), and Associate Professor Per Mattsson (email: per.mattsson@it.uu.se).

Please submit your application by 25 March 2024, UFV-PA 2024/408.

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