Master Thesis - AI, Federated Learning Schema, PSU Power Headroom Forecasting In Radio Base Stations

Posted date:  Sep 14, 2022
Location: 

Luleå, Norrbotten, Sweden

Ericsson Research

 

In the research area Artificial Intelligence (AI) we study and develop Machine Learning and Artificial Intelligence technologies for intelligent systems. Our research spans technologies for intelligent automation, novel applications of ML/AI to differentiate Ericsson's portfolio and the study and development of frameworks for AI in products and services. We work with business units and market areas, and collaborate with academia and research institutes. The Architectures and Frameworks group in Luleå, Sweden, have the next generation AI platform, tools and frameworks as main focus.

 

Join us!

 

About this opportunity

 

Thesis Background

 

Federated learning allows to update a machine learning model by using collective experience without affecting the privacy of their data. Moreover, it can also be exploited to ‘personalize’ the model to the usage profile. In this project it will be explored the application of federated learning to radio base stations power consumption forecasting models.

 

What you will do

 

The thesis is suitable for one student and would involve the following steps (can be adjusted according to research interest):

  • Literature review, identifying relevant concepts and algorithms for power consumption forecast and analysis of federated learning state-of-the-art use cases.
  • Implement a statistical or machine learning model to forecast Radio Base Station's power consumption and measure the computing resources needed to be trained and make inferences.
  • Model and simulate a federated learning-based deployment scheme for the forecast model.
  • Identify the important KPIs and evaluate their overall performance under different use case scenarios

 

You will bring

 

  • High motivation, student who seeking a challenging research work with the freedom to propose and develop their own ideas.
  • MSc student, preferably in machine learning, statistics, mathematics, physics, computer science, electrical engineering or similar areas.
  • Knowledgeable on federated learning concepts and time series analysis. Excellent programming skills in Python, R, Julia or C++. Familiarity using the Unix terminal and Git. Docker and Kubernetes are a plus.
  • Experiences with libraries as TensorFlow, PyTorch, Scikit-Learn. Communication networks and power systems knowledge is a plus.
  • Interest in building end-to-end prototypes and concepts.
  • Be fluent in English.

 

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 never seen before solutions 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.

 

Work location: Luleå. Please state in your application when you can start.

Recruiter: Niclas Persson, niclas.persson@ericsson.com. Please note that we can not accept any applications through email.

 

Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we nurture 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 and Affirmative Action employer, learn more.

Primary country and city:  Sweden (SE)   ||  Sweden : Norrbotten : Luleå  
Req ID: 696102  

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