Teams at Buck Institute for Research on Aging

Conduct aging and age-related disease research using proteomic workflows and liquid chromatography-mass spectrometry. Analyze human tissues, cancer and Alzheimer’s disease samples, and biofluids to understand mechanisms and discover biomarkers. Independently operate mass spectrometry instrumentation, perform protein quantification, support technology development, and process research data within collaborative projects focused on aging, neurodegeneration, proteostasis, kidney injury, metabolism, and sirtuins.

Investigate mitochondria-lysosome crosstalk in aging and cellular senescence using cell biology, advanced imaging, quantitative analysis, genetics, mechanistic perturbation, high-content microscopy, and computational approaches. The researcher will study organelle communication, cellular homeostasis, and age-associated dysfunction across yeast, human cells, and animal models, while contributing to multidisciplinary research pipelines involving automation, high-throughput screening, and machine-learning-based image analysis.

Develop and apply advanced AI and machine learning methods to aging biology research. Build computational approaches for large-scale biological and imaging data, including computer vision, segmentation, deep learning, generative AI, diffusion, multimodal, and foundation models. Analyze complex datasets, identify biologically meaningful patterns, collaborate with experimental and computational researchers, and help shape research at the intersection of AI, imaging, and aging.