PhD in Machine Learning, Deep Learning, or a related area.
Strong technical understanding of state-of-the-art generative AI models, with a particular focus of multi-modal models.
Demonstrable ability to drive high-quality research insights through publications in top-tier machine learning conferences and journals (e.g. NeurIPS, ICML, ICLR, AAAI, ICCV, ECCV, CVPR, JMLR).
Hands-on experience in implementing and empirically evaluating deep learning approaches in PyTorch.
Effective communication skills and ability to work in a collaborative environment.
Preferred Qualifications:
Demonstrable research expertise in any of the following fields: AI fairness, AI interpretability and/or transparency, model adaption (e.g. PEFTs), data-centric AI, evaluation methods.
The ability to approach technical problems and design solutions with a multi-disciplinary perspective.
Passion for ensuring the inclusion of marginalised communities in AI technologies.
Previous experience working in a multi-disciplinary team with diverse skill sets.
Contribution to open-source code projects (e.g. on GitHub).
Responsibilities
Undertake cutting-edge research in deepening our understanding of generative AI models, including the development of technical approaches to ensure they perform equally well in all scenarios.
Write research code to develop and validate new approaches, or develop novel theoretical and practical insights.
Collaborate with a diverse and multi-disciplinary team.
Clearly communicate research ideas and results in writing, such as research papers, presentations, or research notes for internal and external audiences.