Masters Thesis, 30-45 HP Scientific Machine Learning for Physics-Informed Inverse Problems in SAR imaging
Job Location:
Monthly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
Masters Thesis 30-45 HP: Scientific Machine Learning for Physics-Informed Inverse Problems in SAR imaging
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.
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 youll have the opportunity to collaborate with experienced engineers and specialists gaining invaluable practical experience while making a tangible contribution to our growth and development.
Scientific machine learning (SciML) is an emerging discipline that integrates learned algorithms with physical laws and domain knowledge enabled through multiple core areas within scientific computing. SciML tackles several well-known issues in ML related to e.g. model accuracy efficiency and interpretability. SciML is fundamentally connected to inverse problems that naturally arise in synthetic aperture radar (SAR) imaging where many key challenges are related to the notion of ill-posedness. SAR systems generate high-resolution imagery by emitting microwave pulses by processing the returned echoes. To reconstruct an image from observed raw data (complex phase histories) is a highly ill-posed inverse problem.
In this project you will develop high-performance computing (HPC) simulation tools from SciML in the context of electromagnetic wave scattering problems that arise in SAR imaging. Radar wave imaging is of critical importance to e.g. situational awareness and threat detection. It amounts to solving an inverse problem where given some output observations (e.g. raw sensor data) one tries to uncover the latent (hidden) signals (e.g. image) that produced that data. The corresponding forward model is a simulation method typically based on a mathematical representation (e.g. partial differential equation) that given a set of inputs produces or predicts observations. Inverse problems are typically ill-posed i.e. their solutions are highly unstable with respect to small changes in the observed data. Addressing ill-posedness is key for the development of robust inverse reconstruction methods that form the backbone in many applications such as SAR imaging. The main challenge is to develop reconstruction methods that have significantly better performance while being computationally feasible bearing in mind the time-critical nature of common radar applications.
In this work the focus is to develop various frameworks to facilitate large scale forward simulations. Throughout the project you will use modern tools drawing from high-performance computing numerical analysis and machine learning. You will aid in a larger effort aimed at developing novel methods for large-scale data-driven inverse problems based on SciML.
We are seeking highly motivated students with a strong foundation in any of the following areas: computer science machine learning applied mathematics statistics physics or any related field. You should have strong programming skills and be familiar with common machine learning frameworks such as PyTorch and/or JAX. Familiarity with GPU programming is highly advantageous. You are able to communicate efficiently both orally and in addition you are able to work independently and as part of a dynamic team.
At Future Technologies EW and Systems we are developing next-generation intelligent sensor systems for electronic warfare. We are a group of staff engineers specialists and researchers specializing in multiple areas such as signal processing operations analysis applied mathematics and machine learning.
For more information please visit profile This Master Thesis is suitable for 1-2 students with interest in scientific computing applied physics/mathematics and machine learning. You are at the end of your Masters program and about to start your Master Thesis work for 30--45 HP. We provide the support and guidance you need to translate your theoretical knowledge into practical solutions. Join us and become a driving force behind Saabs 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. 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 25500 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 Contact information Neda Tooloutalaie Head of Future Technologies Mobile: Alexander Lindmaa Supervisor Mobile:
