Graduate Student
The approach involves exploring hardware modifications to event cameras, specifically
aimed at increasing sensitivity and signal strength for nanoparticle detection and
tracking. Alongside these hardware advancements, the project requires a strong focus
on advanced modeling and data analysis, starting with the development of a
comprehensive digital twin of an event-based sensor operating within a dark-field
microscopy environment. Building on these models, the work dives into native event-
based data formats by using point clouds and graph-based representations. Ultimately,
these combined efforts will drive the exploration of novel, alternative methods to
accurately derive nanoparticle diameters directly or indirectly from the continuous event
stream. The hardware, software, and data contributions to this project will support US-
based event camera vendors in designing specialized event cameras tailored for
microscopy applications and label-free high-throughput nanoparticle measurements.
Hardware Modifications and Software design for Event Camera Nano-particle Characterization
- PhD Candidate in Physics, Computer Science, or Electrical Engineering with 3 or
more years of relevant experience. - Expertise in PyTorch /Python and experience with neuromorphic computing.
- Familiarity with event cameras, particle tracking, or diffusion analysis is a plus.
- Ability to build and deploy complex software solutions for scientific imaging
applications. - Strong oral and written communication skills and strong presentation skills.
Key responsibilities will include but are not limited to:
- Designing software solutions for event camera nanoparticle tracking employing
native event-based representations (point clouds and graph neural networks) - Collecting new experimental data and generating simulations for model training
- Developing and implementing fused frame-event signal methods using beam
splitters - Simulating various point spread functions to support network training
- Exploring hardware modifications for event cameras such as optical modulation
with a prism or mirror to boost event camera signal in low light. - Exploring camera bias optimization to balance signal strength and sharpness
- Create presentation material of the results