Pilar Navarro Ramírez

AI Research Scientist at Panacea Cooperative Research
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Contact Information
us****@****om
(386) 825-5501
Location
ES
Languages
  • Inglés Full professional proficiency
  • Alemán Elementary proficiency
  • Español Native or bilingual proficiency
  • Francés Limited working proficiency

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Bio

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Credentials

  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    deeplearning.ai
    Sep, 2020
    - Nov, 2024
  • Interactivity with JavaScript
    University of Michigan
    Sep, 2020
    - Nov, 2024
  • Structuring Machine Learning Projects
    deeplearning.ai
    Sep, 2020
    - Nov, 2024
  • Introduction to CSS3
    University of Michigan
    Aug, 2020
    - Nov, 2024
  • Introduction to HTML5
    University of Michigan
    Aug, 2020
    - Nov, 2024
  • Neural Networks and Deep Learning
    deeplearning.ai
    Aug, 2020
    - Nov, 2024

Experience

    • Spain
    • Research Services
    • 1 - 100 Employee
    • AI Research Scientist
      • Dec 2022 - Present

    • New Zealand
    • Environmental Services
    • 1 - 100 Employee
    • Volunteer
      • Nov 2022 - Present

      Collaboration as a computer scientist in the development of the Koster Seafloor Observatory project. Collaboration as a computer scientist in the development of the Koster Seafloor Observatory project.

    • Spain
    • Higher Education
    • 700 & Above Employee
    • Beca de colaboración en el Departamento de Ciencias de la Computación e Inteligencia Artificial
      • Nov 2021 - Sep 2022

      Development of a deep learning-based software for fully automatic segmentation of prostate cancer lesions, both clinically significant and not clinically significant, in prostate T2-weighted MRI. Various real clinical MRI images publicly available together with their associated ground truth segmentation masks were used to train, calibrate and evaluate deep learning models for the task of segmenting the prostate zones and prostate tumors. Development of a deep learning-based software for fully automatic segmentation of prostate cancer lesions, both clinically significant and not clinically significant, in prostate T2-weighted MRI. Various real clinical MRI images publicly available together with their associated ground truth segmentation masks were used to train, calibrate and evaluate deep learning models for the task of segmenting the prostate zones and prostate tumors.

Education

  • Universidad de Granada
    Bachelor's degree in Computer Engineering, 8.5
    2016 - 2022
  • Master D
    Curso superior de zoología
    2022 - 2024
  • Universidad de Granada
    Bachelor's degree in Mathematics, 8.15
    2016 - 2022
  • University of Duisburg-Essen
    Computer Science, 8.8
    2018 - 2019

Community

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