Our laboratory applies computational, and machine learning methods to understand the impact of seizures and abnormal brain activity on outcomes in critically ill patients with brain injury. Our goal is to improve treatment strategies and the clinical care of these patients. Analysis of large datasets (including EHR datasets, hospital and claims based databases) and neurophysiology data (including electroencephalopgraphy-EEG) is central to these efforts.

Specific efforts focus on developing methods for automatically abstracting data including clinical variables (eg medication doses, radiology, laboratory data) and clinical outcomes (eg medication adverse effects) on a large scale; creating tools to predict clinical outcomes in patients with seizures and abnormal EEG patterns treated with anti-seizure medications; and understanding the relationships between abnormal patterns of brain activity (EEG patterns) and neurological deterioration. Efforts will also focus on creating tools for data mining of structured and unstructured clinical data, including free text reports and clinical notes. This will include use of natural language processing. Efforts will also include signal processing, including EEG data processing and annotation.

The ideal candidate will have excellent analytical and troubleshooting skills, knowledge of software development methodologies and systems design, and a desire to solve complex and large-scale problems to make important scientific and technical contributions to medicine.

This position involves a range of opportunities, including:

- Analysis of EEG and other physiological time series data

- Statistical modeling and analysis of diverse medical data

- Software development and code management

- Management of a large physiological database

- Management of a multi-center electronic health record database

We are seeking a full-time postdoctoral fellow with a strong interest in applying clinical research and medical informatics. Applicants should possess a PhD in a relevant discipline (computer science, machine learning, epidemiology, electrical engineering, or applied mathematics) and have established an excellent research profile in signal processing or machine learning. This position is initially available for a period of two years.

In addition, the ideal applicant should have a strong programming background (e.g. Python, R, SQL, MATLAB) and the drive and enthusiasm to learn about clinical neuroscience by assisting in state-of-the art patient-centered research.

PhD in a relevant discipline such as statistics, machine learning, electronic engineering, computer science, or applied mathematics.

The candidate will work within a team of physicians, clinical research coordinators, and biomedical and electrical engineers, and computer scientists. May share responsibility for mentoring more junior lab members.

The candidate will not have budgetary responsibility but will need to design projects and identify equipment for projects within a budgetary scope and in liaison with a staff assistant performing purchase orders.

Work will be performed in several of the MGH Intensive Care Units and in laboratory space on the MGH campus.