Job Description
Our seasonal, full-time research interns working will work in one or more of our active areas of research, which include:
- Biological Transport Networks: The Biological Transport Networks group focuses on understanding transport mechanisms in living organisms. Our primary interests lie in modeling the function and development of vascular networks across multiple scales, and in understanding the dynamics of neuronal growth. We utilize advanced computational and theoretical techniques, including large-scale network simulations and methods from topological data analysis, to address a wide range of problems. Examples of our work include modeling the circulatory systems of developing embryos, exploring how intracellular transport influences neuronal development, quantifying extensive datasets of brain microvasculature, and understanding the effects of nonlinearities within vascular networks.
- Biomolecular Design: The Biomolecular Design team applies the principles that underlie the function of natural biological macromolecules to design artificial and synthetic macromolecules with new, desired functions. This serves as the ultimate test of our understanding of macromolecular folding and function, while simultaneously giving rise to useful molecules for medicine, materials, or manufacturing. The team focuses on both the development and application of computational tools to our current focus areas; the design of cyclic peptides, peptoid-foldamers, hinge-proteins, and catalysts. During the summer internship, interns can expect to work on one of the many design application projects based on their interest as well as project availability using various state of the art simulation, machine learning, or quantum computing tools.
- Biophysical Modeling: The Biophysical Modeling group focuses on the modeling and simulation of complex systems that arise in biology and soft condensed matter physics. Areas of interest include the dynamics of complex and active materials, and aspects of collective behavior and self-organization in both natural systems (e.g., inside the cell) and synthetic ones. To address these, often in close collaboration with experimental collaborators, we build numerical and theoretical models from the ground up, revealing how the known mechanics of individual components give rise to collective behavior.
- Developmental Dynamics: The Developmental Dynamics group combines experiments, theory and computing to elucidate the contributions of encoded genomic instructions and self-organizing physical mechanisms to embryonic development. Its theoretical and computational work is designed to integrate and abstract rapidly accumulating heterogeneous datasets, to propose critical tests of multiscale regulatory mechanisms, and to guide our own genetic and imaging experiments. The group’s research is organized around three main themes: the mechanistic modeling of pattern formation and morphogenesis; the synthesis and decomposition of developmental trajectories; and the modeling of human developmental defects.
- Genomics:The Genomics group works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include developing deep learning approaches for genome interpretation; development of methods for multi-omic data analysis and integration with phenotypic and clinical data; and machine learning approaches for network modeling and regulatory module detection. These and other methods are developed in tight collaboration with experimental biologists, biomedical scientists, and clinicians and are applied to specific biological problems, both fundamental and biomedical.
- Structural and Molecular Biophysics: The Structural and Molecular Biophysics team, a collaborative effort between CCB and the Center for Computational Mathematics (CCM) uses computational tools to study biological macromolecules, running long timescale molecular simulations and developing statistical analysis and machine learning tools to better capture the dynamics of these molecules and understand their biological function. Areas of interest include in particular statistical mechanics, membrane protein structural biology, protein modeling with flexibility, cryo-electron microscopy, thermodynamics, modeling the effect of mutations, and intrinsically disordered proteins.
- Computer Vision & Machine Learning: Recent work involves instance segmentation, tracking and lineage construction of pre-implantation mouse embryogenesis and extending this to other organisms. This has included extensive benchmarking of segmentation approaches, developing a generalized approach to detect mitotic events and optimizing track associations using simulated annealing.
QUALIFICATIONS
Education
- Applicants must be currently pursuing or recently completed a bachelor’s, master’s, or be only in the initial stages of their PhD program (first 1-2 years) in applied mathematics, statistics, computational biology, biophysics, computer science, engineering, mathematics physics, or related disciplines.
Related Skills & Experience
- Demonstrated abilities in mathematical modeling, biophysical analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis
- Ability to do original and outstanding research in computational biology
- Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists
- Strong oral and written communication, data documentation, and presentation skills
- Excellent collaborative and interpersonal skills.
Disclaimer: Applications will be received until this position is filled.
