The Division of Neurocritical Care in the Department of Neurology at Yale University is inviting applications for a two-to-three-year postdoctoral position with experience in one or more of the following: physiologic signal processing (e.g., EEG), image processing and machine learning methods. The applicant would be joining the lab of Dr. Jennifer A. Kim MD-PhD, which focuses on predicting complications and poor outcomes after acute brain injuries injuries, such as trauma, stroke or brain hemorrhage, using data from multiple diagnostic modalities.
The main goal of the research will be development of signal processing and/or image processing and machine learning methods for prediction of complications and poor outcomes after acute brain injury. The position will require both the processing of already collected datasets as well as assistance with the ongoing effort of establishing a larger data set of recordings and images, which may include some practical implementation, execution, storage, and transfer of new data.
Applicant must have a background in signal processing, or image processing and some experience in statistical modeling or machine learning methods. Programming experience in Matlab, Python and/or R is a prerequisite. Experience with EEG, other brain physiology recordings and/or MRI analyses is an advantage but not required. Experience with a range of machine learning methods including random forest and deep learning methods also an advantage, but not required.
Good oral and written English communication skills, the ability to work both independently and as part of a team, is a prerequisite. Candidates must be self-motivated. Responsibilities will include design, implementation and dissemination of research including data processing, modeling, and drafting manuscripts.
This appointment as a postdoctoral researcher requires academic qualifications at the PhD or MD level, preferably in Computational Neuroscience, Engineering, Bio/Statistics, Computer Science, or related areas.