Research identify and evaluate state-of-the-art LLMs for effective correct and grounded edge-based mission planning.
The key responsibilities of this role include conducting a review on existing LLMs and their applications in edge computing. This involves identifying the strengths and limitations of current LLMs in the context of mission planning and reviewing case studies and existing implementations of LLMs in similar domains. The selected LLMs will be evaluated and documented based on criteria developed for assessing their effectiveness correctness and grounding in the application of mission planning on the edge.
The research field and applications of Large Language Models has literally exploded during the last years. This has led to models that are getting more and more capable whilst their size and hardware requirements continue to grow smaller. While more and more capable at the time of writing most state-of-the-art open-source models still does not have the performance that is required to add true value in the application of edge-based mission planning. E.g. they lack in inference performance quality of the results or they are still too resource-heavy for effective execution on an edge device.
This work aims to study the field of open-source LLMs and too narrow down on models that are truly capable in the application of edge-based mission planning. This work will include identification and evaluation of identified models and to assess their performance based on measures relevant for mission planning on the edge. The identified models will be evaluated based on criteria developed for assessing their effectiveness correctness and grounding in in the mission planning domain. Prototypes or proof-of-concept systems will be developed to demonstrate the application of LLMs in edge-based mission planning and their performance and scalability will be evaluated.
This work will include the following work packages:
1. Identification of state-of-the-art in open-source LLMs relevant for edge-based mission planning.
2. Identification of relevant measures and tests for validation of identified models.
3. Demonstration and documentation of the results
This Master Thesis is suitable for one student. You are at the end of your master studies in computer science software engineering or applied mathematics. A specific interest in AI and planning applications is merited.
This position requires that you pass a security vetting based on the current regulations around/of security protection. For positions requiring security clearance additional obligations on citizenship may apply.
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Karina Wandt Manager
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Ella Olsson Master Thesis Supervisor
Company in Brief Saab serves the global market with world-leading products, services and solutions from military defence to civil security. With operations on every continent, Saab continuously develops, adapts and improves new technology to meet customers’ changing needs. At Saab we ... View more