The Johns Hopkins iMIND and CIS are seeking a postdoctoral fellow with research interests in developing computational methods and applying them to study biomarkers and gain mechanistic insights into brain disorders. We invite applications from highly motivated statisticians, mathematicians, or computational biologists with a research background in neuroscience, as well as related fields such as genetics, cancer, or immunology.

The research will develop computational methods in order to decode the molecular mechanisms underlying psychosis. It will focus on stable and interpretable methods for patient classification and stratification, for small-sample biomarker discovery, for modeling signaling networks, and for reducing the complexity of high-throughput and high-dimensional biological data. The research will also integrate preclinical and clinical data such as mice electrophysiological data and human resting-state functional magnetic resonance imaging data.

Applicants must hold a Ph.D. in mathematics, statistics, bioinformatics, or related field. They should have some experience, supported by strong publications, in the development of computational methods and exhibit a good knowledge of statistics and be proficient in programming. Experience in analyzing next generation sequencing data, familiarity with neuroimaging data and knowledge of neuroscience are not required but will be a plus.

The position is funded by an NIH P50 Center Program. Remuneration will follow the NIH recommendations for postdoctoral fellow salary and will range from $62K to $74K depending on the level of experience.

The Johns Hopkins Initiative for Medical Innovation and NeuroDiscovery (Johns Hopkins iMIND) is an academic initiative to address fundamental questions in brain science, in particular the major knowledge gap between basic neuroscience and patient care in clinical settings. To effectively address the gap and build a new conceptual framework for research, we employ a cross-disciplinary approach that optimally employs and integrates a wide range of current and emerging technologies in conjunction with best expertise.

The Center for Imaging Science (CIS) in the Johns Hopkins Whiting School of Engineering sits at the intersection of mathematics, computer science, biomedical engineering, and electrical engineering. Its members are developing tools to extract patterns and meaning from various types of large-scale datasets, and are applying this knowledge to guide new advances in facial recognition software, vision-based navigation systems for robots and self-driving cars, disease diagnosis and treatment, and more. Using mathematical and computational approaches, CIS researchers are pioneering new discoveries in four major areas: medical image analysis, computer vision, computational biology, and statistical learning.