The ideal candidate will have strong intellectual curiosity and passion to solve real-world problems in healthcare using machine learning. Responsibilities will include: Co-development of an internship project in collaboration with the supervisor Design, implementation and evaluation of new machine learning methods and models Presentation and communication of research findings Currently enrolled in a PhD program in areas such as computer science (e.g. machine learning, deep learning, signal processing), medical imaging, computational biology, medicine Prior experience with deep learning frameworks (e.g., PyTorch) and some familiarity with software engineering practices (e.g. git) Passion for healthcare and medicine Experience with real-world healthcare data. Ability to work and learn in a collaborative and diverse environment Representation learning self-supervised learning, unimodal or multimodal learning Interpretability methods for deep learning (e.g. mechanistic interpretability, intrinsically interpretable methods, representation engineering, circuit discovery or rule extraction) Design, training, or evaluation of large unimodal or multimodal transformers Biomedical imaging such as radiology, computational histopathology Computational biology including -omics, bioinformatics, when coupled with deep learning Clinical data integration or multimodal fusion Large language models for healthcare and medicine, biomedical natural language processing, post-training of LLMs/RLAIF AI for scientific discovery, including hypothesis generation, biomarker discovery Causal machine learning Track record of publication in conferences or journals within machine learning and/or healthcare