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 firstname.lastname@example.org.