Postdoctoral Researchers in Distributed Machine Learning

At Uppsala University (https://www.uu.se/institution/elektroteknik), we conduct successful research and education in areas such as control engineering and signal processing, machine learning, industrial IoT, 6G communication, and wireless sensor networks, as well as research and education in renewable energy, electric vehicles, life sciences, smart electronic sensors, and medical systems. The Department of Electrical Engineering is an international workplace with about 170 employees who all contribute to solving important technical challenges in energy and health at the Ångström Laboratory.

We are pleased to announce up to two postdoctoral positions, located at the Department of Signals and Systems, Department of Electrical Engineering.

Project Description:

Distributed machine learning enables collaborative learning across multiple devices without exchanging raw data, ensuring privacy and reducing communication costs. In applications such as networked autonomous systems and mobile robotics, learning over wireless networks presents significant challenges due to limited communication bandwidth and channel variations, limited computational resources of IoT devices, the heterogeneous nature of distributed data, and randomly time-varying network topologies.

The goal of the project is to design and analyze fast, low-complexity, and communication-efficient algorithms for distributed learning and optimization that are adaptable to the constraints posed by wireless networks. Furthermore, malicious nodes in distributed systems can disrupt the training process by sending false data and through Byzantine attacks. Designing Byzantine-resistant distributed machine learning algorithms under privacy constraints is therefore also a challenging task that will be investigated within this project.

The project is interdisciplinary in nature and requires tools from distributed optimization and control engineering, wireless communication and networks, signal processing, statistical machine learning, random matrix theory, as well as methods for privacy and security. The project will contribute to advancing the research frontier in robust distributed learning over wireless networks and also to developing design guidelines for practical learning algorithms.

Responsibilities:

The main responsibilities for these positions include:

• Conducting theoretical and applied research in distributed optimization and machine learning, particularly developing new federated and fully distributed machine learning and optimization algorithms, performing performance analyses of these algorithms on synthetic and real datasets, and designing Byzantine-resistant and privacy-preserving algorithms for distributed learning
• Writing high-quality technical research papers for publication in highly regarded journals such as IEEE Transactions, Automatica, and Journal of Machine Learning Research, as well as in leading international conferences in the field
• Participating in the dissemination of research results through departmental seminars and conference presentations nationally and internationally
• Teaching at undergraduate and graduate levels corresponding to a maximum of 20% of full-time

Qualifications:

• The applicant must have a PhD or foreign degree deemed equivalent to a PhD in electrical engineering, computer engineering, or applied mathematics with a focus on control engineering and optimization or statistical machine learning
• The degree must be obtained by the time of the employment decision. Preference should be given to those who have obtained their degree within the last three years. When calculating the three-year frame, the deadline for applications is the starting point. If there are special reasons, such a degree may have been obtained earlier. Special reasons include leave due to illness, parental leave, or union duties, etc.
• Very good analytical and mathematical skills and good knowledge in several of the following areas: control theory, convex optimization, statistical machine learning, signal processing, and wireless communication, differential privacy, and homomorphic encryption
• Good programming skills in MATLAB or Python (mandatory) and C++ (meritorious)
• Excellent ability to express oneself verbally and in writing in English
• Ability to conduct independent research and effectively collaborate with other group members

About the Employment

The employment is temporary for 2 years according to the central collective agreement. The scope is full-time. Start date 2026-06-01 or by agreement. Place of employment: Uppsala.

For further information about the positions, please contact Professor Subhrakanti Dey ([email protected] (mailto:[email protected])). Information about the Department of Signals and Systems can be found at http://www.uu.se/institution/elektroteknik (http://www.uu.se/institution/elektroteknik).

Application should include:

• A personal letter explaining why you are interested in the position and how it matches your qualifications, along with a brief description of your previous research experience
• CV
• Publication list
• Other relevant documents, such as names of 2–3 referees

Welcome with your application by March 20, 2026, UFV-PA 2026/304.

Uppsala University is a broad research university with a strong international position. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all 7,600 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of the most exciting workplaces in the country.

Read more about our benefits and what it is like to work at Uppsala University here (https://uu.se/om-uu/jobba-hos-oss/).

The employment may be subject to security clearance. A prerequisite for employment is that the applicant is approved during the security clearance.

We kindly decline offers of recruitment and advertising assistance.

Applications are received in Uppsala University's recruitment system.

Union representatives: Saco-S - [email protected] (mailto:[email protected]), Seko - [email protected] (mailto:[email protected]), ST (OFR/S) - [email protected] (mailto:[email protected])