My name is Zaid Al Hakioui. I am a biomedical engineer specialized in data science, artificial intelligence, and computational modelling applied to healthcare and biomedical research. I have experience working with medical imaging, clinical datasets, and AI-driven tools to support data-driven decision-making and improve diagnostic and research workflows.

Education

  • Interuniversity Master’s Degree in Health Data Science (UB, UPC, UAB, UdG, UdL, UVic-UCC, URV, Universite Grenoble Alpes)
    Sep 2025 - Present
  • B.Sc. in Biomedical Engineering, Universitat Pompeu Fabra
    Sep 2020 - Jul 2024

Experience

  • AI Research Engineer, Biorce (PharmaTech)
    Apr 2026 - Present
    • I joined Biorce in April 2026 after finishing my stage at Alma Medical Imaging.
    • I work on AI-driven solutions for pharmaceutical and clinical workflows, with a focus on reliable deployment and practical impact.
    • I contribute to research and development initiatives that connect data science, medical context, and product needs.
  • Junior AI Engineer, Alma Medical Imaging
    Sep 2024 - Apr 2026
    • Built an AI orchestration platform integrating 3 segmentation models and 21 clinical inference services for automated CT/MRI analysis in production workflows.
    • Developed datasets of 150+ imaging studies (CT and MRI) in collaboration with hospitals to support training and validation of medical AI systems.
    • Deployed GPU-accelerated Linux pipelines, enabling sub-minute inference per study for real-time clinical usability.
    • Implemented radiology workflow automation (segmentation, reporting assistant, and longitudinal CT/MRI comparison), reducing manual preprocessing and supporting radiologist evaluation.
  • Biomedical Data Science Intern, Hospital de la Santa Creu i Sant Pau (Barcelona, Spain)
    Apr 2022 - Aug 2024
    • Processed around 100 CT scans to build cardiac cavity segmentation datasets for coronary AI models.
    • Implemented MONAI + U-Net pipelines achieving Dice scores between 0.845 and 0.969 across structures.
    • Developed reproducible preprocessing and postprocessing workflows for structured medical AI training datasets.
  • Computational Neuroscience Intern, Research Group on Neuronal Dynamics - MedTech (UPC)
    Feb 2023 - Jun 2023
    • Modeled spiking neural networks using differential equation simulations and neural mass models.
    • Implemented numerical experiments and analysis pipelines in Python and MATLAB for population-level neural dynamics studies.
    • Generated simulation-based insights on synaptic transmission and stability regimes in neural systems.

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