Master Thesis, 30 HP: UGV Traversability Planning with Large Language Models
Teknik, data och digitalt · Data, AI och analys · Maskininlärning · Artificiell intelligens · Mjukvaruutveckling
I korthet
Ansvarsområden
- Investigate how multimodal large language models can be utilized for planning the movement of unmanned ground vehicles (UGVs) through off-road terrain.
- Conduct a literature study on the subject.
- Implement an LLM-based planning system.
- Evaluate the system quantitatively and qualitatively against publicly available datasets for autonomous off-road ground vehicles.
Krav
- Student in the final stage of Engineering Physics, Computer Science, AI/ML or equivalent.
- About to begin a 30 hp thesis project.
- Must pass a security vetting based on current regulations.
- Potential additional obligations on citizenship may apply for positions requiring security clearance.
Önskade kvalifikationer
- Theoretical knowledge and fresh perspectives.
- Enthusiasm for cutting-edge technology.
Förmåner
- Opportunity to collaborate with experienced engineers and specialists.
- Gain invaluable practical experience.
- Make a tangible contribution to company growth and development.
- Support and guidance to translate theoretical knowledge into practical solutions.
Färdigheter
Om Saab
As a leading defence and security company, we offer solutions that range from the depths of the oceans to high in the sky, on land and in cyberspace, to keep people and society safe. Empowered by our 22,000 talented people, we constantly push the boundaries of technology to create a safer, more sustainable and more equitable world. We design, manufacture and maintain advanced systems in aeronautics, weapons, command and control, sensors and underwater systems. We are headquartered in Sweden and have major operations all over the world and are part of the domestic defence capability of several…
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SaabPublicerade jobb
för 3 dagar sedan
Upphör
om 1 månad
Anställningstyp
Praktik
Arbetsform
På plats
Erfarenhetsnivå
Junior
Platser
Linköping, Sweden
Kvalifikation
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