The suprachiasmatic nucleus (SCN) is a cluster of roughly 20,000 neurons that creates and maintains the circadian signal that acts as a master clock for the organism. The neurons in the SCN act as oscillators and the network of connections between them are key to the robustness and resilience of the circadian signal. Our work, in collaboration with Rae Silverâ€™s lab at Barnard College, uses neurobiological data to create mathematical models of the neural function with an interest in discovering constraints on the network structure. Funding is available for both part-time and full-time positions.

Current work is supported by NSF IOS-1730508, the E.E. Just Program at Dartmouth College, and Undergradaute Advising and Research at Dartmouth College.

Two types of positions - data analysis and modeling - are available. Students interested in data analysis should have a basic familiarity with MATLAB and linear algebra (e.g. MATH 22). Students interested in modeling should be comfortable programming in MATLAB and have completed both linear algebra (MATH 22) and differential equations (MATH 23).

Positions for Summer 2018 are filled. However, there will be both part and full time positions available in Fall, Winter, and Spring terms. To apply, look for advertisements through UGAR or apply to Professor Pauls directly with:

- Letter describing interest in program such as your own mathematical background and interests, career goals, the type of position you are interested in, and your qualifications for it.
- Unofficial transcripts or degree audit showing courses taken and grades.

Professor of Mathematics

Principle Investigator

Andrew is a double major in Mathematics and Computer Science at Dartmouth College.

Pierre is an E.E. Just Scholar working with the research group in Summer 2018.

Yanling is an E.E. Just Scholar working with the research group in Summer 2018.

Lizzie is a third year graduate student working in applied mathematics.

- Phase analysis of SCN data from coronal, sagittal, and horizontal SCN slices.
- Intermediate-scale modeling of neural networks within the SCN.

- Analysis of synthetic data from model oscillator networks.