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Pilar Navarro RamírezPN

Pilar Navarro Ramírez

AI Engineer

EUR 278/Tag
Madrid, ES
0-2 Jahre

Durchschnittliche Reaktionszeit: 1h

Über Pilar

I work at the intersection of Artificial Intelligence and social impact, with a strong belief that technology should be used to serve meaningful causes. Until recently, I worked as an AI Researcher at Panacea Cooperative Research and volunteered part-time as an AI Engineer at Wildlife.ai, where I explored how deep learning can contribute to fields like medical imaging, anthropology, and wildlife conservation.

My academic background includes a dual degree in Computer Science and Mathematics, an honors Bachelor’s thesis on deep learning for the automatic segmentation of prostate cancer in MRI images, and an international academic experience in Germany, which helped me grow both personally and professionally.

I’ve led and contributed to projects involving medical imaging, facial analysis, image translation, wildlife detection using deep learning, and distance estimation from facial indexes, always with a strong focus on scientific rigor and social relevance.

I’m open to new opportunities where I can continue growing as an AI engineer, particularly in projects related to visual data. I’d be especially excited to join initiatives that have a positive impact on the environment, animal health, conservation, or societal well-being.


Outside of my technical work, I am deeply committed to personal development and emotional intelligence. I regularly explore topics like psychological well-being, self-leadership, and communication. I enjoy guiding others through mentorship and personal growth, and I’m always seeking to evolve as both a scientist and a human being.

  • Spanisch

    Muttersprachlich oder zweisprachig

  • Englisch

    Verhandlungssicher

  • Deutsch

    Konversationssicher

Nur remote
Führt Projekte hauptsächlich remote aus

Projekt- und Berufserfahrung

  • Panacea Cooperative Research
    Artificial Intelligence Researcher
    November 2022 - Dezember 2024 (2 Jahre und 1 Monat)
    Granada, Spain
    • Principal researcher in a CBCT‑to‑CT image translation project using deep neural networks such as Pix2Pix, CycleGAN, and CUT, with the goal of generating CT images realistic enough to allow accurate segmentation of bone and facial structures. Results included segmentations with a mean Dice coefficient of 0.915.
    • Automatic classification of simulated skull‑face overlays into correct or incorrect using various classic deep classification networks, achieving an accu‑ racy of 97.8%.
    • Automatic analysis of facial features in simulated images to improve the robustness of an automatic skull‑face overlay algorithm. This work optimized reliability under more varied and realistic conditions.
    • Principal developer of a system to estimate camera‑to‑subject distance using facial indices and models such as Random Forest and Gradient Boost‑ ing, reducing the mean absolute error from 32 cm (state of the art) to 5.6 cm.
    • Designed a system for automatic exclusion of negative candidates based on facial index comparison and prediction intervals estimated using various machine learning algorithms. Achieved reliable exclusion of over 60% of negative cases under more variable conditions than those tested in the previous study.
    • Provided guidance and supervision to an intern student.
    • Presented research results at national and international conferences for non‑technical audiences.
    Deep Learning Machine learning Computer Vision Pytorch Research
  • Wildlife.ai
    Data Scientist
    November 2022 - Mai 2024 (1 Jahr und 6 Monate)
    New Zealand
    • Trained YOLO models to detect and classify fish species in underwater footage. Performed data quality and model performance analysis. Produced statistical summaries for biologists.
    • Provided support in the development of interactive Jupyter Notebooks to simplify code usage for non‑technical biologists.
    Python Data science Software Development Deep Learning
  • FruitpunchAI
    AI engineer
    April 2023 - Juli 2023 (3 Monate)
    • AI for European wildlife challenge: As part of the "model exploration" team, conducted research and applied various deep learning models (YOLOv8, YOLO‑NAS, and multiscale vision transformers) to detect and classify European wildlife in camera trap images.
    • Contributed to the publication of project results.
    Teamwork Data science artificial intelligence Deep Learning Machine learning

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Ausbildung und Abschlüsse

  • Bachelor's Degree in Mathematics and Computer Science
    University of Granada
    2022
    Bachelor's Degree in
  • Bachelor's Degree in Computer Science
    University of Duisburg‑Essen

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