[visionlist] Full-time RA position in Xiao lab at American University in DC
bei.xiao at gmail.com
bei.xiao at gmail.com
Tue Jul 11 14:33:48 -04 2023
Our lab is hiring a full-time RA position in an NIH-funded project on
computational modeling of human material perception.
Please see the full ad below and I am happy to answer any questions.
The Xiao Computational Perception Lab
<https://sites.google.com/site/beixiao/> in the Department of Computer
Science at American University is seeking a full-time Research Assistant/
Lab Technician for an NIH-funded project on the computational modeling of
human intuitive physics, material perception, and immersive material
perception in VR/AR.
The RA is to pursue research projects of his/her own as well as provide
support for research carried out in the Xiao lab. Possible duties include:
Python coding for experiment interface and behavioral data analysis
Collecting data for psychophysical experiments
Building VR/AR experimental interfaces with Unity3D
Training machine learning models
This is an ideal position for someone interested in gaining research
experience in perception science and computational modeling before applying
to graduate school or an industrial research position. The position comes
with a salary and full benefits. Full-time staff at AU can take computer
science courses for tuition remission. This position is initially for
contract and can be extended. Starting date is September 1st, 2023, or soon
The ideal candidate should have a Bachelor's degree in neuroscience,
psychology, computer science, engineering, or a related field.
The candidate should have strong programming skills in Python and is
familiar with Numpy, Pandas, and other numerical libraries. Having
experience deep learning with PyTorch is a plus.
Experience with statistical methods (linear models, multivariate
Experience with psychophysics research is not required but would be
The Lab and Facility
Xiao Lab studies both human and computer vision with an emphasis on
material perception and recognition. The lab currently has a few ongoing
Learning latent representation of human perception of material
Material and object perception in infants and children with behavioral
and EEG methods (collaborating with Dr.Laurie Bayet
Volumetric Capture Studio
Uncertainty estimation in few-shot learning in text classification
Prediction of clinical trial outcomes with human experts and machine
The Xiao Lab is located in a state-of-the-art technology building, which is
home to computer science, physics, applied math, and a design and build
lab. The lab has high-performing GPU workstations, haptic phantom devices,
VR headsets, and 3D printers.
Washington, DC, is the US capital and has a vibrant scene of computational
cognition and computer vision research (e.g., NIH, NIST, Johns Hopkins
University, George Washington University, and the University of Maryland).
How to apply
Please submit your application, including a CV, and a cover letter
describing your background, experience, and motivation - preferably in PDF
format, and the names of two references that have agreed to be contacted.
Please apply to Prof. Bei Xiao at bxiao at american.edu.
Representative Recent Publications：
1. Liao, C, Sawayama, M, Xiao, B. (2023) Unsupervised learning reveals
interpretable latent representations for translucency perception. PLOS
Computational Biology. Feb 8, 2023. PDF.
2. Zhang, X, Lei, S, Alhamadni, A, Chen, F, Xiao, B, and Lu, CT.
(2023) CLUR: Uncertainty Estimation for Few-Shot Text Classification with
Contrastive Learning. ACM SIGKDD 2023. PDF upon request.
2. Liao, C, Sawayama, M, Xiao, B. (2022) Crystal or Jelly? Effect of Color
on the Perception of Translucent Materials with Photographs of Real-world
Objects. Journal of Vision. PDF
3. He, J. Zhang, X., Shuo L. Wang, S, Huang, Q., Lu, C-T, Xiao, B. (2022)
Semantic Editing On Segmentation Map Via Multi-Expansion Loss. Neurocomputing.
501,306-317. PDF. <https://arxiv.org/abs/2010.08128>
Bei Xiao, PhD
Computer Science & Center for Behavioral Neuroscience
American University, Washington DC
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