The Rockefeller University's Data Science Platform (DSP) aims to support and advance research objectives by providing expertise in machine learning, artificial intelligence, and deep learning. The Director will lead a team to identify and address the needs of university researchers in applying these quantitative approaches to diverse datasets and research areas, including leading the Fisher Center AI Platform's efforts in Alzheimer's disease research.
Requirements
- Broad knowledge on recent advances in artificial intelligence, such as foundation models (including large-language models, vision-language models, genomic foundation models, etc.), generative models, self-supervised learning, large-scale distributed training, efficient deep learning, with research expertise in at least one relevant field.
- Basic knowledge of the deep learning tool stack, such as PyTorch, Python, CUDA, Linux, and computing cluster management.
Responsibilities
- Consult on experimental design and best usage, with emphasis on best practices for proper analysis and reproducibility;
- Develop and lead training/classes in ML/AI fundamentals and techniques; adapt and evolve training to best serve the changing needs of the research community;
- Identify, improve, and/or create appropriate training data sets for ML/AI projects;
- Advise researchers and collaborators on method selection and implementation, as well as on how to direct research projects to ensure alignment with emerging advancements in deep learning;
- Maintain awareness of advances in the field and their applicability to problems of interest to members of Rockefeller’s research community;
- Identify and apply for appropriate grant funding opportunities;
Other
- Ph.D. or equivalent degree in data science, machine learning, artificial intelligence, computer science, electrical engineering, statistics or a related field.
- A minimum of ten (10) years of research experience in identifying, formulating and solving biological problems through the application of advanced artificial intelligence technologies.
- At least two (2) years of leadership experience.
- A strong publication record in top-tier venues focused on machine learning, artificial intelligence or their domain-specific applications, such as AAAI, NeurIPS, ICML, ICLR, IJCAI, KDD, ICCV, ECCV, CVPR, EMNLP, ACL, MICCAI, IJCV, TPAMI, TIP, ACM-MM, Nature, Nature Methods, Science and equivalent conferences and journals.
- Must possess excellent communication and interpersonal skills.
