Postdoc Position in Biomedical Deep Learning


Updated on 10/01/2019


I have an immediate opening for a post-doctoral fellow to engage in a federally-funded multi-year project in biomedical deep learning. My research group at Drexel University focuses on developing state-of-the art deep learning-based algorithms and applying them to a wide range of problems. Ongoing projects are:


       Design of novel and application of existing cutting-edge natural language processing (NLP) models (e.g. Transformer, BERT, GPT-2, XLNet) for biomedical information retrieval and text summarization. This is an unprecedented opportunity to harness unstructured information from millions of documents, use this information to guide generic drug product development.


       Development of novel and application of existing deep generative models (e.g. GANs, VAEs) to explain the high-dimensional structure and time course of neural population activity. The focus is on the extraction of low-dimensional temporal patterns in high-dimensional spiking and local field potentials datasets in visual attention and working memory tasks, and the development of new tools for causal inference (e.g. copula Granger-causality).


The ideal candidate should have, or be about to receive, a Ph.D. in a relevant discipline with substantial computational experience (especially in deep learning/NLP). Programming skills in Python and/or Julia are essential. Knowledge of state-of-the-art NLP/text mining methods and/or deep learning frameworks (e.g. TensorFlow, PyTorch) is advantageous but not required.


Interested individuals should email a curriculum vitae and a brief statement of research interests to Dr. Hualou Liang at