Job Description

Join our Team

About this opportunity:

Modern radio networks can be used as radar-like sensors to detect and classify moving objects — a capability broadly referred to as Integrated Sensing and Communication (ISAC). Our team builds the machine-learning models behind this capability.

As we deploy the models to different locations and extend it to more object classes, two operational problems dominate our research agenda:

• Cross-location generalisation: models trained on data from one location do not always transfer to another. Every new location currently requires labelled data-to attune for the new location — a slow and expensive step that gates deployment.
• Multi-class extension: adding new object classes without degrading performance on existing ones. It involves architectural trade-offs that are not obvious.

We are offering three related master's thesis opportunities on these problems, all rooted in production machine learning. Please indicate in your application which direction you are most interested in. The exact scope of each project will be refined together with the student based on their interests, background, and the state of our data and models when the project starts.

What you will do:

• Direction 1: Cross-location Generalisation of a spectral image Classifier

Benchmark modern domain-generalisation methods for image data. Implement source-free adaptation methods that adjust the model at deployment using only unlabelled data from the new site.

• Direction 2: Cross-location Generalisation of a time-series Classifier

Benchmark modern domain-generalisation methods for time-series data. Implement source-free adaptation methods that adjust the model at deployment using only unlabelled data from the new site.

• Direction 3: Modular Classifier Design for Multi-Class Object Classification

Benchmark methods for multi-class classification models such as Hierarchical tree, Mixture-of-experts and flat softmax. Evaluate how well they perform when adding additional classes and seeing new locations.

The skills you bring:

• You are pursuing a master's degree in computer science, machine learning, applied mathematics, or a related field.
• Good understanding of modern machine learning, particularly deep learning.
• Strong programming skills, familiarity with deep learning frameworks, and an interest in experimental, research-oriented work.
• You are analytical, curious, and able to work independently while communicating your results clearly.

Why join Ericsson?At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply?Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: Sweden (SE) || Stockholm

Req ID: 792543