Postdoctoral position in Barcelona on AI in Medical Imaging - AFRICAI-RI

Description

Offer Description: We are offering an exciting postdoctoral position at the University of Barcelona, within the Artificial Intelligence in Medicine lab, new trustworthy AI solutions for for multimodal medical imaging to support diagnosis and clinical decision-making in respiratory diseases, including tuberculosis and pneumonia. Concretely, we offer: A research position in beautiful Barcelona and its Mediterranean climate. Research experience within a prestigious university (1st position in Spain). Cutting-edge research in AI for healthcare in one of the most dynamic research groups in Europe (12 active projects including an ERC grant). An international research environment by joining a multi-cultural team representing all continents. Opportunities to collaborate with international and inter-disciplinary collaborators as part of the European projects. Support in career development ( e.g. grant applications, supervision of PhD students). Travelling opportunities to scientific events, project meetings and international stays. Freedom to independently conduct research and contribute with own ideas. Flexible working hours, with possibility to telework. Competitive salary equivalent to Assistant or Associate Professor at the University of Barcelona, depending on experience. Contract duration until the end of the projects (minimum four years), with the possibility to extend the contract as part of other grants within the lab. The requirements for the position are: Strong background in machine learning and deep learning, with experience in developing and evaluating AI models for biomedical imaging and/or healthcare applications. Experience in medical image analysis, including tasks such as image classification, segmentation, anomaly detection, or predictive modelling. Experience working with heterogeneous biomedical datasets, including medical imaging (e.g. X-ray, ultrasound, MRI) and associated clinical data. Good understanding of data preprocessing, harmonisation, model validation, and challenges associated with multi-centre datasets. Proficiency in Python and relevant machine learning and data science frameworks (e.g. PyTorch, TensorFlow, Scikit-learn). Familiarity with trustworthy AI principles, including model robustness, generalisability, fairness, and/or explainability, is desirable. Experience with federated learning, domain adaptation, or privacy-preserving AI approaches is considered an advantage. Scientific track record demonstrated through peer-reviewed publications in relevant journals and/or leading international conferences in artificial intelligence, machine learning, and medical imaging (e.g. MICCAI, CVPR, NeurIPS, ICML). Excellent written and spoken English, with the ability to communicate and present scientific results effectively. Furthermore, knowledge in any of the following areas are desirable (but optional): Ability to work independently, take initiative, and drive research activities from conceptualisation through implementation, evaluation, and dissemination. Strong teamwork skills and willingness to actively participate in the scientific activities of the research group. Ability to collaborate effectively with multidisciplinary and international partners, including clinical researchers, technical experts, and other stakeholders. Previous experience in research leadership, including coordinating research activities, international collaborations, and project deliverables, is desirable. Strong interest in developing robust, trustworthy, and clinically relevant AI solutions for medical imaging, particularly in diverse healthcare settings. Ability to contribute to the coordination of research activities, supervision of students, and dissemination of scientific results. Strong scientific communication, writing, and presentation skills, including the ability to represent the research group at project meetings and scientific events. Motivation to contribute to international collaborative research and the translation

Sectors

Source

Source: euraxess.ec.europa.eu