.The Centro Nacional de Investigaciones Cardiovasculares Carlos III (F.S.P) (CNIC) has been conceived to develop research of excellence, competitive and of international relevance in relation to cardiovascular diseases. The CNIC is a research center of 24,000 m2, located in Madrid, with more than 6,000 m2 for laboratories equipped with a state-of-the-art infrastructure and equipment.CNIC leads the Project, AI POCVUS-REACT, Artificial Intelligence-assisted point of care vascular ultrasound device for personalized cardiovascular prevention.We are looking for two junior-level AI Developers with expertise in developing models for medical imaging applications. You will work on the development, optimization, and deployment of AI models, with a focus on improving the accuracy and efficiency of image analysis tools used in healthcare.This contract is funded by Mecanismo de Recuperación y Resiliencia de la Unión Europea-Next Generation, in the framework of the call "Solicitud de Proyectos de I+D de Excelencia en Inteligencia Artificial de la Secretaría de Estado de Digitalización e Inteligencia Artificial."Functions:Develop, train, and optimize deep learning models for analyzing medical images.Integration of AI/ML models for real-time analysis and decision-making.Preprocess large datasets, perform data augmentation, and fine-tune model architectures for improved performance.Collaborate with medical professionals and researchers to understand domain-specific requirements.Optimize models for deployment in real-world applications, considering efficiency, speed, and accuracy.Document model development, experiments, and results for reproducibility.Contribute to the development of tools and pipelines for data processing, model training, and evaluation.Learning and supporting senior developers, model development, preprocessing, and working on well-defined tasks.Mandatory Requirements:Bachelor's degree in Computer Science, Biomedical Engineering, Data Science, or a related field.Valuable Requirements:C1. Knowledge and/or experience in Python.C2. Knowledge and/or experience in AI/ML Integration (e.G., PyTorch) for building and training deep learning models.C3. Knowledge and/or experience in Machine Learning. Solid understanding of key ML concepts, including supervised/unsupervised learning, CNNs (Convolutional Neural Networks), and image segmentation/object detection.C4. Knowledge and/or experience in Data Handling. Experience with medical imaging formats like DICOM and using libraries such as OpenCV, PIL, or SimpleITK.C5. Knowledge and/or experience in Deep Learning. Knowledge of deep learning architectures such as YOLO, UNet, non-UNets, GANs, etc.C6. Knowledge and experience in Version Control. Familiarity with Git for code management and collaboration.C7. Knowledge and/or experience in AI development or relevant projects. Professional experience not mandatory, academic projects and internships are acceptable.C8