The Synthetic Biomolecular Materials and Machines group is hiring a postdoctoral scholar for a project advancing the capabilities of dynamic biomolecular materials. The ideal candidate is highly driven and holds a strong interest in using cutting-edge methods for quantitative design, imaging and modeling of dynamic molecular assembly and organization. The members of our research group strive to work closely as a team and to create and maintain a vibrant, productive, and supportive culture conducive to scientific and personal growth. Projects in the Schulman lab are interdisciplinary and the candidate will be expected to contribute to the lab’s collaborative and nurturing learning environment.
Details: This research project will involve:
1. DNA Nanotechnology: The design of DNA nanostructures, scaffolded DNA origami, self-assembly and structural characterization using methods such as epi-fluorescence imaging, electron microscopy and confocal microscopy.
2. Design of biomolecular reaction networks: Design of coupled biochemical reaction networks involving DNA strand displacement, transcription and enzyme-directed nucleic acid synthesis/degradation and characterizing network dynamics using time-lapse fluorescence
3. Kinetic and structural simulation and design: The use of biochemical networks to direct hierarchical DNA self-assembly processes. Design of these processes will involve coupled ordinary and/or partial differential equation models and could be supplemented by the use of
Brownian dynamics simulations.
4. Time-lapse fluorescence imaging and quantitative analysis: Dynamics of material formation and reorganization will be captured via time-lapse fluorescence imaging. The resulting images will be used for quantitative characterization; the candidate will use and develop
automated image processing methods to characterize specific aspects of structure formation and
5. High-throughput characterization and automation: This project will be accelerated by an interest in and commitment to methods of high-throughput characterization and automation and methods to interpret data collected from high-throughput experiments.
Tagged as: Chemistry, Engineering, Life Sciences, Physics
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