Thesis work 30hp - Designing embedded real time Machine Learning architecture
Om jobbet
Introduction
Thesis work is an excellent way to get closer to Traton Group and build relationships for the future. Many of today's employees began their Traton journey with their degree project.
Background
In the era of software defined vehicles, intelligence and adaptability are the key differentiators in delivering value to the end customer. The power of high-performance embedded computing systems can be leveraged for data analysis and real time machine learning that can improve vehicle functions and deliver high customer value. To optimize for cost and time all the systems in a vehicle cannot have high performance architecture, thus the need of an effective distributed online learning system. The project goal is to design such a system to improve the range estimation function for ICE (Internal Combustion Engine) vehicles.
Objective
Education/program/focus
Current MSc in Engineering within embedded systems or similar, with experience in machine learning algorithms
Number of students: 1
Start date for the thesis work: January/February 2026
Estimated time required: 6 months
Contact persons and supervisors
Abhijith Sriram, Embedded application developer, Abhijith.sriram@scania.com
Application:
Your application must include a CV, personal letter and transcript of grades
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
Requisition ID: 22340
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country/Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: Hybrid
Thesis work is an excellent way to get closer to Traton Group and build relationships for the future. Many of today's employees began their Traton journey with their degree project.
Background
In the era of software defined vehicles, intelligence and adaptability are the key differentiators in delivering value to the end customer. The power of high-performance embedded computing systems can be leveraged for data analysis and real time machine learning that can improve vehicle functions and deliver high customer value. To optimize for cost and time all the systems in a vehicle cannot have high performance architecture, thus the need of an effective distributed online learning system. The project goal is to design such a system to improve the range estimation function for ICE (Internal Combustion Engine) vehicles.
Objective
- Design an optimal CAN based communication strategy between two systems, that implements the distributed learning system. The learning system will be made up of two ECUs, one implementing the range estimation function and the other hosting the high performance capabilities.
- Investigate and select a machine learning model to be used for the range estimation function.
- Benchmark the solution in the vehicle.
- Present a roadmap for the learning system to be deployed in production.
Education/program/focus
Current MSc in Engineering within embedded systems or similar, with experience in machine learning algorithms
Number of students: 1
Start date for the thesis work: January/February 2026
Estimated time required: 6 months
Contact persons and supervisors
Abhijith Sriram, Embedded application developer, Abhijith.sriram@scania.com
Application:
Your application must include a CV, personal letter and transcript of grades
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
Requisition ID: 22340
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country/Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: Hybrid
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