Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges? At Saab, we believe that innovation thrives on new ideas, and your master thesis project could be the spark that ignites our next technological breakthrough.
Your role
We recognize the immense value that students bring to our company. Your academic rigor, combined with your enthusiasm for cutting-edge technology, allows you to approach problems with a unique and insightful lens. At Saab, you'll have the opportunity to collaborate with experienced engineers and specialists, gaining invaluable practical experience while making a tangible contribution to our growth and development.
Background
Achieving an air-situational picture of high quality – a task in which radars play a central role – is essential to ensuring the integrity of the air-space, and in turn, the safety of people and society. A surveillance radar works by emitting energy that is reflected by targets of interest as well as other objects in a surveillance volume, giving rise to unlabeled sets of detections of unknown origin. Through repeated measurement of the surveillance volume and the use of target-tracking algorithms, with a foundation in Bayesian estimation, a situational picture emerges in the form of an a posteriori probability distribution describing the air-space. Competitive overall system performance relies on having accurate target models and being able to perform filtering with respect to these models within a limited computational budget.
This results in a performance boundary, where the availability of filters with acceptable computational cost limits the possibility to use more expressive models, identifying the objective this thesis – namely to explore concepts to push this performance boundary.
In particular, focus will be put on enabling computationally efficient particle filters. Particle filters enjoy guarantees of asymptotic exactness and would hence in principle be highly suitable for state estimation in complicated target models. Unfortunately, their computational efficiency relies on the availability of suitable proposal distributions, which is a circumstance that rarely is fulfilled. However, recent advances in generative modeling potentially creates a path to overcome this historic limitation – with significant impact on overall system performance.
Project
The project aims to explore and evaluate the viability of methods for optimal proposals in particle filters, with special focus on system models described by stochastic differential equations. This includes understanding the necessary theory, implementing learning routines, training neural stochastic differential equation and comparing with baseline approaches.
We are interested in both practical implementations and theoretical understanding/results, and the project will have a focus depending on your interests and strengths.
Your profile
This Master Thesis is suitable for 2 students.
You are in the end of your technical master’s education in Engineering Physics, Engineering Mathematics, or similar, with an interest for advanced mathematics and numerical methods. Courses in Stochastic analysis and Bayesian statistics are meriting, as well as practical experience of deep learning.
We provide the support and guidance you need to translate your theoretical knowledge into practical solutions. Join us and become a driving force behind Saab's technological advancements!
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.
What you will be a part of
Explore a wealth of possibilities. Take on challenges, create smart inventions, and grow beyond. This is a place for curious minds, brave pioneers, and everyone in between. Together, we achieve the extraordinary, each bringing our unique perspectives. Your part matters.
Saab is a leading defence and security company with an enduring mission, to help nations keep their people and society safe. Empowered by its 25,500 talented people, Saab constantly pushes the boundaries of technology to create a safer and more sustainable world.
Saab designs, manufactures and maintains advanced systems in aeronautics, weapons, command and control, sensors and underwater systems. Saab is headquartered in Sweden. It has major operations all over the world and is part of the domestic defence capability of several nations. Read more about us here
Kindly observe that this is an ongoing recruitment process and that the position might be filled before the closing date of the advertisement.