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phD
3D ultrasound imaging using orthogonal row and column addressing of the matrix array for ultrasonic NDT
This thesis is part of the activities of the Digital Instrumentation Department (DIN) in Non-Destructive Testing (NDT), and aims to design a new, fast and advanced 3D ultrasound imaging method using matrix arrays. The aim will be to produce three-dimensional ultrasound images of the internal volume of a structure that may contain defects (e.g. cracks),...
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phD
CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees. Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask...
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phD
Combined Software and Hardware Approaches for Large Scale Sparse Matrix Acceleration
Computational physics, artificial intelligence and graph analytics are important compute problems which depend on processing sparse matrices of huge dimensions. This PhD thesis focuses on the challenges related to efficiently processing such sparse matrices, by applying a systematic software are hardware approach. Although the processing of sparse matrices has been studied from a purely software...
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phD
Efficient Multimodal Vision Transformers for Embedded System
The proposed thesis focuses on the optimization of multimodal vision transformers (ViT) for panoptic object segmentation, exploring two main directions. The first is to develop a versatile fusion pipeline to integrate multimodal data (RGB, IR, depth, events, point clouds) by leveraging inter-modal alignment relationships. The second is to investigate an approach combining pruning and mixed-precision...
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phD
Automatization of quantum computing kernel writing for quantum applications
The framework of Hamiltonian simulation opens up a new range of computational approaches for quantum computing. These approaches can be developed across all relevant fields of quantum computing applications, including, among others, partial differential equations (electromagnetism, fluid mechanics, etc.), quantum machine learning, finance, and various methods for solving optimization problems (both heuristic and exact). The...
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