UAB invites applications for a fellowship focused on advancing AI applications in Digital Health and Digital Industry. Fellows will work on pioneering projects such as developing AI infrastructure for efficient inference on Edge AI platforms (e.g. Smartphones) for real-time (RT) diagnosis and interpretation (e.g. skin, eyes, teeth) of medical images from new multispectral RGB+IR image sensors, high-resolution ultrasound, etc. that can be extended to RT applications for high-speed production lines (e.g. laser marking, X-ray inspection). This position offers two main research directions as options (listed in Key Responsibilities). Candidates are expected to select their preference to any of those two options.
Key Responsibilities (2 options):
- Design, development and deployment on Ai models on Edge AI platforms for real-time inference on medical images and personalized healthcare.
- SW-HW development to improve Edge AI acceleration on heterogeneous CPU-GPU-FPGA platforms.
Qualifications:
- PhD in Computer Engineering, Computer Science, Bioinformatics, Electronics Engineering, Biomedical Engineering, or related fields.
- Strong knowledge of AI techniques, including deep learning training and inference, transformers and AI deployment.
- Skills on application development on mobile/embedded and Edge AI platforms.
- Interest in interdisciplinary collaboration at the intersection of technology for healthcare and digital industries.
Possible Principal Investigators:
- Jordi Carrabina leads projects related to digital technologies and devices applied to health and industry using different imaging technologies for diagnosis.
Email: jordi.carrabina@uab.cat
Web: https://portalrecerca.uab.cat/en/persons/jordi-carrabina-bordoll-17
Keywords: Edge AI inference optimizations. Real-time imaging on embedded/edge AI platforms. High-resolution ultrasound medical devices. Digital Health platforms.
- David Castells leads projects related to the design and implementation efficient HW/SW computation applied to Edge AI using CPU (RISC-V), GPU and FPGA platforms with applications to applied to real-time applications in the biomedical and industry domains.
Email: david.castells@uab.cat
Web: https://portalrecerca.uab.cat/en/persons/david-castells-rufas-8
Keywords: Energy-efficient designs for edge AI acceleration. Real-time AI deployment techniques (computer vision, laser marking systems, sequence alignment). High-performance data processing systems.
