- Salary
- $74k – $81k/yr
- Location
- Novato, CA
- Type
- Full-time
- Department
- Education
- Education
- PhD
- Closing date
- Aug 6, 2026
- Source
- ApplyToJob
Description
Mitochondria Lysome Function and Aging Research
Position Summary
The Buck Institute for Research on Aging is seeking a highly motivated Postdoctoral Researcher to investigate mitochondria–lysosome crosstalk in the context of aging and cellular senescence. This position will focus on understanding how communication between mitochondria and lysosomes regulates organelle function, cellular homeostasis, and age-associated decline.
The successful candidate will use a combination of cell biology, advanced imaging, quantitative analysis, genetics, and mechanistic perturbation approaches to uncover how mitochondria–lysosome interactions contribute to organelle and cellular dysfunction associated with hallmarks of aging. The project will also explore new strategies to restore organelle communication and intervene in aging-associated phenotypes in the context of both normal aging and age-related diseases.
The candidate will join a highly collaborative and multidisciplinary team that integrates biology, biophysics, imaging, quantitative analysis, computational biology, and artificial intelligence. This environment provides strong opportunities to combine experimental discovery with quantitative and computational approaches, particularly through high-content imaging, automated microscopy, and large-scale image analysis.
About the Zhou Lab
The Zhou Lab is an intentionally multidisciplinary research group that uses model systems ranging from budding yeast and cultured human cells to animal models to study fundamental mechanisms of aging. Our team brings together researchers from cell biology, biophysics, computer science, artificial intelligence, and other quantitative disciplines. We view this cross-disciplinary environment as a major strength, creating opportunities for shared learning, creative problem-solving, and discoveries that would be difficult to achieve within a single field alone.
Our lab uses a diverse range of cutting-edge approaches, including super-resolution imaging, high-throughput screening, cryo-EM, simulations, genetics, and machine learning. Current research areas include systems biology of aging, organelle communication, and proteome plasticity.
Recently, the lab has built automated research pipelines that combine liquid-handling robotics, high-throughput cell manipulation, automated super-resolution microscopy, and machine-learning-based image analysis. These platforms enable large-scale imaging and quantitative analysis of molecular and cellular aging phenotypes.
We are looking for candidates who are passionate about uncovering basic principles of molecular and cellular biology and who are excited to work at the interface of organelle biology, aging research, advanced imaging, and quantitative analysis.
Recent Publications
The Buck Institute for Research on Aging is the first independent biomedical research institute in the United States devoted solely to research on aging. Located in Novato, California, the Buck brings together scientists across multiple disciplines to study the mechanisms of aging and age-related disease, to help people live better, longer.
The Buck Institute is an equal opportunity employer. We strongly encourage applications from candidates whose experiences and perspectives will contribute to the diversity and excellence of our community.
To Apply
Please submit:
Position Summary
The Buck Institute for Research on Aging is seeking a highly motivated Postdoctoral Researcher to investigate mitochondria–lysosome crosstalk in the context of aging and cellular senescence. This position will focus on understanding how communication between mitochondria and lysosomes regulates organelle function, cellular homeostasis, and age-associated decline.
The successful candidate will use a combination of cell biology, advanced imaging, quantitative analysis, genetics, and mechanistic perturbation approaches to uncover how mitochondria–lysosome interactions contribute to organelle and cellular dysfunction associated with hallmarks of aging. The project will also explore new strategies to restore organelle communication and intervene in aging-associated phenotypes in the context of both normal aging and age-related diseases.
The candidate will join a highly collaborative and multidisciplinary team that integrates biology, biophysics, imaging, quantitative analysis, computational biology, and artificial intelligence. This environment provides strong opportunities to combine experimental discovery with quantitative and computational approaches, particularly through high-content imaging, automated microscopy, and large-scale image analysis.
About the Zhou Lab
The Zhou Lab is an intentionally multidisciplinary research group that uses model systems ranging from budding yeast and cultured human cells to animal models to study fundamental mechanisms of aging. Our team brings together researchers from cell biology, biophysics, computer science, artificial intelligence, and other quantitative disciplines. We view this cross-disciplinary environment as a major strength, creating opportunities for shared learning, creative problem-solving, and discoveries that would be difficult to achieve within a single field alone.
Our lab uses a diverse range of cutting-edge approaches, including super-resolution imaging, high-throughput screening, cryo-EM, simulations, genetics, and machine learning. Current research areas include systems biology of aging, organelle communication, and proteome plasticity.
Recently, the lab has built automated research pipelines that combine liquid-handling robotics, high-throughput cell manipulation, automated super-resolution microscopy, and machine-learning-based image analysis. These platforms enable large-scale imaging and quantitative analysis of molecular and cellular aging phenotypes.
We are looking for candidates who are passionate about uncovering basic principles of molecular and cellular biology and who are excited to work at the interface of organelle biology, aging research, advanced imaging, and quantitative analysis.
Recent Publications
- Yoo S, et al. The Spatiotemporal Proteome Landscape of Aging: Structural determinants of age-sensitive proteome remodeling. bioRxiv [Preprint]. 2026 March 1. DOI: 10.64898/2026.02.26.708310
- Liu, Q., et al. Mitochondria-vacuole coupling contributes to vacuolar acidification and aging. In press, Molecular Cell
- Zheng, F., et al., Metabolic Environment–Driven Remodeling of Mitochondrial Ribosomes Regulates Translation and Biogenesis. Molecular Cell 2025 Nov 6:S1097-2765(25)00853-6.
- Liu, Q., et al. Nascent mitochondrial proteins initiate the localized condensation of cytosolic protein aggregates on the mitochondrial surface. Proc Natl Acad Sci USA. 2023 Aug; 120(31) e2300475120
- PhD in cell biology or related field
- Track record of first-author publications in related field
- Ability to work independently and collaboratively in a multidisciplinary research setting
- Strong verbal and written communication skills
- $73,760 - $81,440 depending on skills and experience
- Comprehensive benefits including medical/dental/vision coverage, paid time off, paid parental leave, 401(k) employer contribution, and a student debt repayment option.
The Buck Institute for Research on Aging is the first independent biomedical research institute in the United States devoted solely to research on aging. Located in Novato, California, the Buck brings together scientists across multiple disciplines to study the mechanisms of aging and age-related disease, to help people live better, longer.
The Buck Institute is an equal opportunity employer. We strongly encourage applications from candidates whose experiences and perspectives will contribute to the diversity and excellence of our community.
To Apply
Please submit:
- A curriculum vitae or resume,
- A cover letter addressing your past research experience, plans, and expectations for working in the lab
- Any relevant publications, projects, or code.
Skills
Machine Learning