PhD student in Automatic Control
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 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.
The research project for the advertised position will be within the area of automatic control and/or machine learning. The topic below is provided mainly to make the advertised position more concrete. We welcome prospective students’ initiatives, and the precise research topic will be decided in a dialog between the applicant and the supervisor after a successful appointment.
Project Description: Closing the loop in real-time cyber-neural systems
Understanding the neurophysiological systems, such as the brain or peripheral nervous system, is key to developing technology capable of restoring or enhancing functioning when neurological issues afflict subjects due to trauma, diseases, or disorders. Non-invasive (e.g., transcranial magnetic stimulation) and invasive (e.g., electrical neurostimulation) technology interact with the neural substrate by deploying stimuli determined upon data collection on the neural activity.
State-of-the-art methodologies are responsive. They deploy an a priori (and offline) design sequence of stimuli after an event of interest (i.e., the so-called open-loop event-trigged control). Therefore, the data collected is mainly used to monitor an event of interest but completely discarded from deciding stimuli to interact with the neural substrate toward a desirable goal.
Converging evidence suggests that the dynamical nature of such systems can be modeled by the so-called fractional-order dynamical systems that account for long-term memory due to the underlying electro-chemical biology. Therefore, developing the control theory foundation for such dynamical systems is of utmost importance.
In this project, we seek to design closed-loop control schemes that can be deployed in devices with low computation and battery capabilities. The closed-loop schemes should leverage the system dynamics and leverage recent developments in model predictive control that enable the satisfaction of medical safety constraints by design. Additionally, the nature of the research is highly interdisciplinary as we seek to transition the methodologies to practice. Therefore, the prospective Ph.D. student will interact with researchers from different fields (i.e., control theory, machine learning, neuroscience, and medicine).
More information is available via the link to the project description.
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%.
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.
Experience and courses in one or more subjects are valued: control theory, linear systems, nonlinear control, robust control, estimation, automatic control, model predictive control, artificial intelligence, machine learning, optimization, neuroscience, and neurophysiology.
Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.
The application must include: 1) a statement (at most 2 pages) of the applicant’s motivation for applying for this position, together with 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.
About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date xx or as agreed. Placement: Uppsala.
For further information about the position, please contact: Associate Professor Sérgio Pequito (email: email@example.com).
Please submit your application by 28 February 2023, UFV-PA 2023/89.
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Submit your application through Uppsala University's recruitment system.
Placement: Department of Information Technology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed salary
Number of positions: 1
Working hours: 100 %
County: Uppsala län
Seko Universitetsklubben firstname.lastname@example.org
Number of reference: UFV-PA 2023/89
Last application date: 2023-02-28
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