Training the next generation of interdisciplinary neuroscientists
The Stanford Interdisciplinary Graduate Fellowship (SIGF) is a competitive, university-wide program that awards three-year fellowships to outstanding doctoral students engaged in interdisciplinary research. Three independent institutes, Bio-X, Wu Tsai Neurosciences Institute, and Sarafan ChEM-H award these graduate fellowships in the biosciences.
The Wu Tsai Neurosciences Institute partners with the Vice Provost for Graduate Education and Stanford BioX to award Stanford Interdisciplinary Graduate Fellowships (SIGFs) in the area of neuroscience. We are grateful to Bio-X and the Bio-X Leadership Council for incorporating the fellowships affiliated with the Institute into their application process.
Learn more about application details and eligibility criteria.
2025 SIGF Application
All applications submitted through the portal will be considered for fellowships affiliated with the Wu Tsai Neurosciences Institute, Bio-X, and Sarafan ChEM-H. Students whose research is especially aligned with the scientific mission of the Wu Tsai Neurosciences Institute should indicate their preference for the Wu Tsai Neurosciences Institute affiliation on their application.
Funded SIGF projects
Understanding cellular responses induced by chronic implantation of electrodes using a novel human neural differentiation platform
Electrodes implanted in the brain have great potential, with applications in neurodegenerative disease, brain-computer interfaces, and more. However, the presence of electrodes in brain tissue causes a response known as gliosis, in which a scar forms around the electrode, reducing its effectiveness and access to neurons.
Leveraging screenomics to identify mental illness: Detecting bipolar disorder through computational analysis of smartphone screen data
Mental illnesses like bipolar disorder affect millions of people around the world, but early symptoms are often difficult to detect. Working across the disciplines of clinical psychology, communication, and computer science, my research will develop a novel computational tool to identify signals of mania and depression in real-time.
