Student Research Assistants in Signal Processing and Machine Learning

Description 

Electrification and digitalisation are among the major areas for the future transition to sustainable societies. The Department of Electrical Engineering conducts successful research and education in these areas - including renewable energy sources, electric vehicles, industrial IoT, 6G communication, and wireless sensor networks, as well as research and education in Life Science, health technology, smart electronic sensors, and medical systems. The Department of Electrical Engineering is an international environment with around 160 employees who contribute to important technological energy and health at the Ångström Laboratory.

We are now looking for up to 2 student research assistants. 

Come and work with us!
The position will be placed at the Division of Signals and Systems, at the Department of Electrical Engineering. Here you will find a friendly work environment where both education and research go hand in hand. We look forward to receiving your application. Join us and build the future with us!

Duties

• The student research assistant will work with the development and implementation of methods in signal processing and machine learning.
• Relevant subject areas include continual learning and collaborative learning in machine learning. They will conduct literature reviews within relevant areas.
• The duties include developing and maintaining code for simulations and experiments.
• The position involves statistical analyses and the evaluation of methods using relevant measures.
• The student research assistant will document results and take part in the preparation of research reports, scientific articles, and presentations. 

Requirements

• To be eligible for employment as a student assistant, you must be admitted to studies at the undergraduate or advanced level.
• This position is directed towards students admitted to the Master’s Programme in Engineering Physics, Master’s Programme in Electrical Engineering, Master’s Programme in Computer and Information Engineering or to students in the Master's Programme in Mathematics, Master’s Programme in Computer Science or Master's Programme in Data Science, with good academic results.

We are looking for candidates with:

• A strong interest in developing new theory and methods for machine learning.
• A strong mathematical background.
• Good proficiency in programming (preferably in Python).
• A structured, self-driven, independent approach to technical work and good collaboration skills.
• Good knowledge of English, both spoken and written.
• The role involves contact with others, and great emphasis will be placed on personal suitability.

Coursework or other experiences with the following subjects are valued: optimization, linear algebra, signal processing, probability, random processes, statistical machine learning, deep learning. 

It is important that you are curious and eager to learn, able to work independently and take responsibility. You should be able to communicate clearly in speech and writing, and plan and organize your work effectively.

About the employment 

The employment is temporary, in accordance with the Higher Education Ordinance, Chapter 5, Section 12. The scope of employment is 50%. Starting date: 2026-01-19 or as otherwise agreed. End date: 2026-05-31. Location: Uppsala.

Application instructions
The application should include 1) a short letter describing yourself, why you are applying for the position, and relevant experience, 2) your CV, 3) a certificate of registration and a transcript of records showing that you are a registered student with good academic results, 4) other documents you wish to include, and 5) names and contact details of reference persons (e-mail address and telephone number). The application should be written in English.

For further information about the position, please contact
Associate Professor, Ayca Ozcelikkale, ayca.ozcelikkale@angstrom.uu.se, +46 18 471 2939.

You are welcome to submit your application no later than October 24th, 2025. UFV-PA 2025/3057.