Machine Learning Postdoc Fellow

Our laboratory applies computational and machine learning methods to investigate the impact of seizures and abnormal brain activity on outcomes in pigs with cortical impact. Our goal is to understand pathological correlates of epilepsy and traumatic brain injury. Analysis of datasets (including video–EEG telemetry, intracellular Chloride, among others) is central to these efforts. 

Specific efforts focus on developing methods for automatically classifying the semiology of pigs in video monitoring as they undergo the development of epilepsy and understanding the relationships between any abnormal behaviors and time after injury or the change in seizure frequency. Efforts will particularly focus on using supervised machine learning approaches including training artificial neural networks via open source software such as Keras, Tensorflow, DeepLabCut, SimBA, TREBA etc. or unsupervised learn...