There are computational neuroscience research positions available in the Yale University Computational Neurophysiology Laboratory. These positions are appropriate for a student who wishes to perform translational research before pursuing a career or graduate studies in data science, neuroscience or medicine (Computer Science, Bioengineering, Electrical Engineering, Neuroscience, MD/PhD, etc.). The students will work closely with one or more faculty of a team composed of a neurosurgeon (Dr. Dennis Spencer), epileptologist (Dr. Robert Duckrow) and engineer (Dr. Hitten Zaveri). Our research is on epilepsy and functional neurosurgery and the development of novel analysis methods and neurotechnology.
Primary roles will include:
• Involvement in ongoing clinical research projects, including data collection, preprocessing and computational analysis
• Opportunities for grant writing, abstract and manuscript preparation, and to present locally and at national conferences.
The student will be exposed to data science, clinical neuroscience, neurosurgery, and related fields depending on the student’s interests.
A major research project is the development of a multimodal brain atlas, requiring the applicant to have the statistical learning, machine learning and deep learning background to understand how human brain signals and images from multiple modalities can be analyzed and fused on a common neuroanatomical framework with other patient derived data. Other projects are focused on computational analysis of complex biomedical time-series, including projects to predict seizures and understand electrical stimulation of the human brain, and the development of novel neurotechnology. The student will gain extensive clinical experience by interacting with epilepsy patients and working collaboratively with the medical team. You are welcome and encouraged to attend department conferences, lectures, and rounds. Mentoring will be provided on how to pursue a career in our respective disciplines, or how to combine basic research and clinical work through a career in academia.
The ideal candidates would be highly motivated, have a strong academic record, an interest in the neurosciences and strong analytic skills (using Python, MATLAB, R, C, C++, etc). Prior experience in biological signal processing, especially electroencephalography (EEG) data, time-series analysis, medical image analysis, statistical learning, machine learning, deep learning, Tensorflow, PyTorch, databases and statistical methods is highly valued. Applications will be reviewed on a first come, first serve basis and strong candidates will be invited to interview.