Current Opportunities at Vanderbilt

A postdoctoral fellow position is open for a project on visually analyzing large-scale data. The prospective candidate will contribute new methods for designing and processing neural network-based representations of fields, for the purpose of analyzing time-varying, multivariate, volumetric data produced by simulation ensembles.

This project is focused on the development of techniques for visually analyzing field-based data, in particular large-scale data that can be compactly represented by coordinate-based neural networks. The goal of the project is to develop processing methods for neural fields, methods that operate directly on the neural representation rather than sampled representations. Research topics consist of: comparison/alignment of neural fields, extracting statistical summaries, contouring/integrating neural fields, topology-based feature extraction, and generative model surrogates of simulations using neural fields.

It is expected that the candidate has a background in data visualization, and in particular the visual analysis of large-scale field data commonly produced in numerical simulations. It is further expected that the candidate has background in machine learning, with an emphasis placed on neural fields - methods for representing and optimizing neural fields.

Moreover, it is highly desirable that the candidate has experience in using neural network-based representations for interactive visualization, namely a background in CUDA-based programming for direct visualization of neural fields.

Candidates should send the following material to Matthew Berger (matthew.berger@vanderbilt.edu):

1. A cover letter that describes the candidate's research background, their interest in the project, and their motivation for pursuing a postdoctoral position.
2. A CV that includes the candidate's publications, as well as contact information for three references.

Applications will be reviewed on a rolling basis until the position is filled.