PhD position in Signal Processing with a Focus on Collaborative Continual Machine Learning - Hiring in process/Finished, not possible to apply

The Department of Electrical Engineering conducts successful research and education in digitalization and electrification including renewable energy sources, electric vehicles, industrial IoT, AI, 6G communication and wireless sensor networks as well as research and education within Life Science, smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with around 170 employees.

The position will be at the Division of Signals and Systems, at the Department of Electrical Engineering. Here you will find a friendly work environment and strong research projects. The Division of Signals and Systems collaborates with Swedish companies - public and private - and stakeholders in the different fields of research. We look forward to receiving your application. Join us and build the future with us!

Project description

Modern systems increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal processing methods, are typically designed for operation in static environments: a model is trained on a fixed dataset and subsequently deployed for inference. However, real-world environments are often dynamic and non-stationary. As a result, the performance of models trained offline tends to deteriorate when exposed to new signal conditions, previously unseen patterns, or changing system characteristics.

This project aims to address these challenges through the development of adaptive learning frameworks for non-stationary environments. In particular, we focus on continual learning, where models are updated as new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting previously acquired knowledge.

We will study collaborative learning scenarios, where multiple devices or sensors jointly process and learn from data streams. Such settings introduce additional challenges, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims to develop learning methods for such collaborative continual learning scenarios. The resulting methods will be analyzed theoretically and validated on representative datasets, with an emphasis on generalization and scalability.

We have an exciting work environment designed by the doctoral student and the research team together. The doctoral student will be supervised by at least two supervisors. The Department of Electrical Engineering also offers a salary supplement in accordance with the local guidelines for doctoral students at Uppsala University.

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

Duties

• The PhD student will carry out research in signal processing and machine learning with a strong emphasis on theoretical foundations.
• The PhD student will actively contribute to setting up the research questions in their doctoral project.
• They will take an active role in planning, implementing and, where necessary, modifying their research project.
• The PhD student will gain advanced and up-to-date specialized knowledge in the area.
• They will develop new theory and methods; and analyze the generalization performance of these methods.
• The work as a doctoral student also includes writing scientific publications and presenting research results orally in various contexts such as project group meetings as well as international conferences.

The main task of a doctoral student is to devote to the doctoral education, which includes both participation in research projects and doctoral education courses. The duties also include participating in teaching and other institutional tasks to a maximum of 20% of the working time.

Qualifications required

To meet the general entry requirements for doctoral studies, you must:

• hold a Master’s (second-cycle) degree in engineering physics, electrical engineering, machine learning, data science, 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. 

Qualifications desired

We are looking for candidates with

• a strong interest in developing new theory and methods for signal processing and machine learning
• strong mathematical background
• good proficiency in programming (preferably in Python)
• good oral and written proficiency in English
• a structured, self-driven, independent approach to technical work and good collaboration skills
• coursework or other experiences in the following subjects are valued: optimization, linear algebra, signal processing, probability, random processes, statistical machine learning, deep learning.  

About the employment

Salary: Individual salary setting.
Starting date: 1 September 2026 or as agreed.
Type of employment: Fixed-term employment in accordance with Chapter 5, Section 7 of the Higher Education Ordinance.
Scope of employment: 100%

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 instructions

Please use the link below. The application must contain:

• A cover letter (max 1 page), in English. The cover letter should include a self-assessment on why you would be the right candidate for this position
• A curriculum vitae (CV).
• A copy of diplomas, certificates and transcripts (translated into Swedish or English).
• The Master’s thesis (or a draft, and/or some other self-produced technical or scientific text), publications, and other relevant documents.
• Contact information for two references (names, emails and telephone number). 

For further information about the position, please contact: Ayca Ozcelikkale, ayca.ozcelikkale@angstrom.uu.se

Please submit your application no later than 2026-05-08. UFV-PA 2026/1113.