Research Scientist - AI and Biology (Biohub SF)

3 days ago

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Chan Zuckerberg Biohub Network

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Accelerating science and developing new technologies to cure, prevent, or manage all disease by the end of the century.

Biochemistry • Bioengineering • Bioinformatics • Biomedical Research • Biophysics

201 - 500

Description

• Develop multimodal data fusion strategies; building, deploying and maintaining state-of-the-art deep learning models to enable translation across multimodal cellular readouts • Develop innovative approaches (deep generative models, foundation models, etc.) for modeling diverse biological data types across multiple scales (cell, tissue, organism) in collaboration with interdisciplinary teams in a hybrid scientific and engineering culture • Writing and presenting work at scientific conferences

Requirements

• PhD degree in Computer Science, Bioengineering, Computational Biology, or a related field with AI/ML focus • 3+ years of experience with Python and relevant deep learning libraries (TensorFlow or PyTorch) • Track record of publications in the field of machine learning applied to biological (omic and/or imaging) data • Experience working with large multimodal datasets (omic and one or more of image and language modalities), and training large AI/ML models • Experience working in Linux environments and familiarity with version control systems (eg. git) • An appetite to follow and build on state-of-the-art research in machine learning, multimodal reasoning, representation learning • Strong professional judgment, problem-solving abilities that adapt to a variety of situations • Strong interpersonal skills with excellent written and verbal communication skills • Ability to respond quickly to shifting circumstances and adjust and prioritize accordingly • Knowledge of learning paradigms beyond supervised learning, such as multimodal data fusion, weakly supervised learning, multitask learning, meta-learning (nice to have) • Experience with deep generative models such as variational autoencoders, generative adversarial networks, and diffusion models (nice to have) • Working knowledge of cell biology and normalization steps associated with one or more omic data modalities (nice to have)

Benefits

• Healthcare coverage • Life and disability insurance • Commuter subsidies • Family planning services with fertility care • Childcare stipend • 401(k) match • Flexible time off • Generous parental leave policy