Up to Three positions for PhD in Automatic 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 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. We strive for all PhD students to get a solid international experience during their PhD.

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

Three 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, although this selection is not binding. The individual research project for each PhD student is decided upon a dialogue between the student and the supervisor. The following topics are offered for this opening:

1st project: Secure Learning and Control Systems
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.

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.

2nd project: Towards an e-Science for Environmental Epidemiology
This project will tackle the considerable challenges of computational modeling to estimate and predict the state of an epidemic. We will do so using data from novel monitoring techniques involving non-traditional sources of data. We will develop nonlinear predictive filters designed specifically with epidemiological models and high noise/low regularity signals in mind. The tools will align with the needs of the multidisciplinary SciLifeLab’s Swedish Environmental Epidemiology Center (SEEC), a pandemic preparedness center for research, competence and technology development.

This PhD position is part of the  eSSENCE-SciLifeLab graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery.

More information can be found via this link to the project’s description.

3rd project: Interfacing with the nervous system at single-cell resolution for the next-generation of artificial retinas
Neurostimulation devices already enable the treatment of several neurological and mental diseases. Unfortunately, there is a dearth of methodologies and tools to unleash their full therapeutic potential. This project seeks to bring together concepts from physics, mathematics, as well as dynamical and control systems, to unveil new insights into operation principles of neurophysiological systems. We will further build on these insights to develop decision-making mechanisms that regulate the neurophysiological systems towards a desirable goal. Ultimately, we will pave the way for efficient and trustworthy neurostimulation devices. In particular, we aim at developing the required mechanisms for the next generation of the artificial retina that will enable the blind to see.

More information can be found via this link to the project’s description.

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

Requirements
A PhD position at the Division requires:

• 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.

Specific requirements for the first project include proficiency in programming (preferably in Matlab or Python), as well as good knowledge of control theory.

Similarly, specific requirements for the second project include proficiency in programming (preferably in Matlab or Python), as well as knowledge in or more of computational science, systems identification, or machine learning.

Lastly, for the third position, the specific requirements are similar to the previous two, and include proficiency in programming (preferably in Matlab or Python), as well as knowledge in optimization, machine learning, dynamical and control systems.

Additional qualifications
For the first project, experience and courses in the following subjects are valued: security and privacy, statistical theory or machine learning, optimization.

For the second project, experience and courses in applied mathematics, statistics, and dynamical systems are also valued.

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 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 or scientific 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 possible starting date of employment. 

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), or Prof. Stefan Engblom (phone: +46 18-471 2754,  email: stefan.engblom@it.uu.se) or Associate Professor Sérgio Pequito, email: sergio.pequito@it.uu.se.

Please submit your application by 20 June 2022, UFV-PA 2022/1728.

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