PostDoc in AI-enabled medical imaging (f/m/x)
Wednesday 29th September 2021
Organization Helmholtz Zentrum München
Title PostDoc in AI-enabled medical imaging (f/m/x)
Email Address firstname.lastname@example.org
Description The Institute of Machine Learning in Biomedical Imaging (IML) focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to fundamentally transform the use of imaging for diagnostics and prognostics. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome.
The successful candidate (f/m/x) will push the limits of machine/deep learning research in one or more of the following hot-topic medical imaging projects that form the four pillars of the new Institute:
Machine learning for advanced imaging: You will develop and explore novel machine learning methods by tight coupling to the physics of imaging formation, including affordable or newly emerging imaging solutions, and/or explicitly embedding disease or response models. Machine learning for early detection and improved outcome: You will develop and explore novel machine learning models to predict whether or not a patient will respond to a specific form of therapy, or whether disease is likely to recur after completed treatment. Machine learning for large population databases: You will develop large-scale machine learning solutions that will link to different diseases, and/or relate different, complementary imaging modalities, biomarkers and molecular features to one another. Machine learning for the clinic: You will innovate methods for domain generalisation, realistic data augmentation, and federated learning, to deploy machine learning methods that have been trained on well curated data more readily to clinical patient data.
The successful candidate (f/m/x) are expected to publish in peer-reviewed journals and to participate in international conferences. The supervision of students and doctoral candidates, contribution to teaching and application for funding are also expected.
Holding an excellent MSc and PhD degree in the field of applied mathematics, computer science/informatics, engineering, (medical) physics or comparable applied / natural sciences Good publications track record in the relevant journals (e.g. IEEE Transactions in Medical Imaging, MedIA, MELBA, Nature Machine Intelligence…) Profound knowledge in machine-learning for (medical) imaging and strong affinity to apply this in the field in one or more of the mentioned medical imaging projects Strong programming skills especially in python and the relevant deep learning libraries (pytorch or tensorflow) Experience in git and other version control Independent, pro-active, structured and solution-oriented work attitude, analytical thinking, above-average commitment and enjoyment of working in an international and collaborative environment Experience and willingness of working in a multidisciplinary team, incl. clinicians and medical physicists Strong scientific communication and presentation skills in English First experiences in third-party fundraising and teaching experience are desirable