BACKGROUND

Our focus in the Spielberg Family Center for Applied Proteomics (SFCAP) is the discovery of protein biomarkers whose relative abundance (or perhaps presence or absence) in a subset of patients can be correlated to their response to existing therapies to treat various diseases. The benefits of a technology to “personalize� medical treatment by accurately predicting outcomes are hard to overestimate in terms of improved outcomes and reduced costs in bringing drugs to market and delivering effective care.

The barriers to personalized medicine via proteomic mass spectrometry are primarily technological in nature. The types of variability that underlie differential responses to treatment may be difficult to detect given normal, but largely uncharacterized, population variation among proteomes of healthy individuals, and variation in the observed data due to the instruments. Extremely high accuracy, resolving power, and sensitivity are required to characterize mixtures as complex as human blood.

At SFCAP, we are looking for talented scientists and engineers capable of developing innovative technologies for extracting information from large complicated datasets that can be used to help doctors provide better care to patients.

Job Description/Responsibilities:

The barriers to personalized medicine via proteomic mass spectrometry are primarily technological in nature. The types of variability that underlie differential responses to treatment may be difficult to detect given normal, but largely uncharacterized, population variation among proteomes of healthy individuals, and variation in the observed data due to the instruments. Extremely high accuracy, resolving power, and sensitivity are required to characterize mixtures as complex as human blood.

At SFCAP, we are looking for talented scientists and engineers capable of developing innovative technologies for extracting information from large complicated datasets that can be used to help doctors provide better care to patients. Key developments in the proteomic mass spectrometry have largely involved developing instrumentation; data analysis methods have not kept pace with these advances. Some ongoing projects in our lab include development of statistical models for human proteomes and the various modalities of mass spectrometry data collection. We have used these models to formulate and solve detection, estimation, calibration, and identification problems. We achieved significant performance gains over heuristic algorithms by using rigorous statistical criteria, such as maximum likelihood, and methods for solving incomplete data problems like the Expectation-Maximization algorithm. There are many opportunities to advance the field by skillful application of statistical signal processing methods.

Necessary skills include a PhD or equivalent in electrical engineering, computer science, physics, or mathematics, and experience in statistical signal processing. Ability to develop proof-of-concept or prototype software is a must. However, candidate will NOT be expected to write production code as the successful candidate will work with a team of software engineers to innovate a production software pipeline. Familiarity with systems biology and protein biochemistry is preferred, but not essential. The successful new hire will be highly self-motivated and creative and able to work effectively in a multi-disciplinary team. In addition, the candidate will develop presentations describing the developed tools and results to share with project collaborators, investigators and potential donors.
Key factors in the success of this job include: high levels of attentiveness, ability to collaborate closely with others and willingness to learn.

Knowledge of signal processing, applied mathematics, computer science, biochemistry, protein chemistry and molecular biology is preferred. Some experience in research laboratory preferred. Senior candidates will be encouraged to develop an independent research program.

HOW TO APPLY

Email: parag.mallick[at]cshs.org