Deep Learning For Computational Biology

The current recruitment is seeking a self-motivated scientist to work on a pattern recognition and classification project using deep-learning techniques such as recurrent neural work (RNN) or long short-term memory (LSTM). The lab collaborates with physician scientists who have generated a large amount of human sleep EEG (electroencephalogram), EMG (electromyography), ECG (electrocardiography), and other types of sleep data with clinical outcomes. The goal of this study is to identify features in those data that are predictive of clinical outcomes. The candidate will work closely with collaborating clinicians and a Staff Scientist in the lab who is experienced with deep learning, machine learning and signal processing.

Qualifications:
Applicants must have a doctoral degree in computer science, electrical engineering, statistics, biostatistics, bioinformatics, neuroscience, or related area with less than five years of postdoctoral research experience. Experience with MATLAB, TensorFlow, Python Keras, and deep learning is preferred.