PhD student in Automatic Control

Are you interested in working on signal processing and mathematical modeling for physiological signals, with the support of competent and friendly colleagues in an 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 PhD position at Uppsala University.

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research  and the Centre for Interdisciplinary Mathematics.

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, having 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 collaborators all around the world, 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 PhD students to get a solid international experience during their PhD.

Project description
In this project, the selected candidate will join us in conducting research on data-driven methods to develop mathematical models of physiological signals. There will be a strong focus on dealing with large patient cohorts and learning to extract information from collected biological data using novel methods from machine learning and system identification. The potential applications will be in cardiology. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor.

This PhD position is part of the eSSENCE 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. eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University.

 The position is also supported by SciLifeLab. SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent.

Duties
A doctoral student will devote their time mainly to graduate education. The rest of the duties may involve teaching at the department, including 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, data analysis, computer science, applied mathematics 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

• interest in developing applications in signal processing and mathematical modeling for physiological signals.
• good communication skills with sufficient proficiency in oral and written English,
• excellent study results,
• high proficiency in programming (preferably in Python),
• personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving are essential

Additional qualifications
Experience and courses in one or more subjects are valued: signal processing, statistical machine learning, system identification, optimization and deep learning.

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 contain:

1. A curriculum vitae (CV),
2. A copy of relevant grade documents (translated into Swedish or English),
3. A list of publications
4. Up to five selected publications in electronic format
5. A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page)
6. Contact information for two references
7. A cover letter briefly describing your motivation for applying for this position and the earliest possible employment date (max 1 page).

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 February 2025 or as agreed. Placement: Uppsala

For further information about the position, please contact: Antônio Horta Ribeiro, email: antonio.horta.ribeiro@it.uu.se, website: https://antonior92.github.io/

Please submit your application by 25 October 2024, UFV-PA 2024/3095.

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