Distributed Systems
Work description
- Design and implementation of artifact simulation models to serve the artifact correction model; - Implementation and optimization of the artifact correction model in magnetic resonance images; - Design and implementation of federated computing environments using NVIDIA Flare and Flower. - Experimental validation of the prototype developed in HPC environments. The tasks described in this work plan require the application and development of concepts and techniques from the field of Biomedical Engineering, typically taught in curricular units that make up the core of the branch in Medical Informatics study plan.
Academic Qualifications
Enrolled in the Masters in Medical Informatics from the study plan of Biomedical Engineering.
Minimum profile required
- Solid knowledge of deep learning architectures and tools (i.e. TensorFlow, PyTorch, GANs, CNNs).- Solid knowledge of medical image evaluation and metrics (i.e. SSIM, NMI).- Solid knowledge of federated learning tools (i.e. Flower, NVIDIA Flare).- Solid knowledge of distributed systems.
Preference factors
- Experience with the Python programming language. - Experience in developing and applying deep learning models. - Experience using federated learning tools. - Experience using HPC environments (e.g. SLURM).
Application Period
Since 18 Apr 2024 to 03 May 2024
Centre
High-Assurance Software
Scientific Advisor
António Luís Sousa