About the job
About the Arc Institute
The Arc Institute is an innovative scientific research organization dedicated to conducting groundbreaking basic science and technology development focused on understanding and treating complex human diseases. Based in Palo Alto, California, we operate independently while collaborating with leading institutions such as Stanford University, UCSF, and UC Berkeley.
We believe that advancing scientific research requires new institutional models. Our approach includes:
- Comprehensive Funding: We fully finance our Core Investigators’ research teams, freeing them from the traditional constraints of project-based external grants.
- Advanced Technology: Recognizing the growing complexity of biomedical research, our Technology Centers develop, optimize, and deploy cutting-edge experimental and computational technologies in partnership with our Core Investigators.
- Exceptional Support: We provide unparalleled operational, financial, and scientific support, enabling researchers to undertake ambitious, high-risk projects aimed at significant advancements in curing diseases such as neurodegeneration, cancer, and immune dysfunction.
- Cultivating a Positive Culture: We prioritize a research environment that nurtures scientific curiosity, a commitment to truth, broad ambition, and collaborative spirit.
Having grown to over 350 staff with more than $650 million in committed funding and a state-of-the-art lab facility, the Arc Institute is poised for rapid expansion in the coming years.
About the Position
The Zhou Lab is seeking passionate, diligent, and inquisitive candidates. Our specialization lies in single-cell epigenomic modeling, utilizing high-throughput single-cell multiomic technologies and computational models to investigate the spatiotemporal dynamics of gene regulation.
The chosen applicant will be pivotal in pushing the boundaries of generative AI applications in biology, focusing on DNA sequences, gene regulation, and perturbation modeling. You will be tasked with developing machine learning models tailored for biological data by leveraging innovative ML architectures, interpretability techniques, and more. Furthermore, your models will be applied to significant computational biology tasks, including genome mining and molecular interaction analysis.
