The Arc Institute is looking to develop advanced machine learning models to predict cell state response to perturbations as part of its Virtual Cell Initiative.
Requirements
- PhD in Computer Science, Computational Biology, Bioinformatics, Machine Learning, or a related field.
- Minimum of 3 years of experience in building machine learning models for large datasets.
- Well-versed in machine learning frameworks such as PyTorch.
- Strong research background with contributions to machine learning conferences (e.g., NeurIPS, ICLR, ICML) or interdisciplinary scientific journals (e.g., Nature, Nature Methods, Science).
- Experience working with biological datasets including single-cell genomics, genomic sequences, bioimaging
- Software engineering experience
- Demonstrated key contributions to the field of predictive modeling of cell states.
Responsibilities
- Build state-of-the-art AI models for understanding how cells respond to perturbation, in collaboration with other ML researchers, engineers and experimental scientists at Arc.
- Stay up-to-date with the latest advancements in machine learning for computational biology and ensure the models built at Arc remain state-of-the-art.
- Guide both the training of models as well as the large-scale generation of new experimental data to train those models, as part of Arc’s Virtual Cell Initiative.
- Collaborate with experimental biologists to ensure that the developed models are grounded in biologically impactful use cases.
- Publish findings through journal publications, white papers, and presentations.
Other
- Strong communicator, capable of translating complex technical concepts to non-technical audiences across disciplines
- Ability to communicate and collaborate successfully with domain experts and ML engineers.
- Motivated to work in a fast-paced, ambitious, multi-disciplinary, and highly collaborative research environment.
- Continuous learner
- PhD in Computer Science, Computational Biology, Bioinformatics, Machine Learning, or a related field.
