Computational Scientist | Miller Brain Observatory - Rockefeller University : Job Details

Organization Overview: The core mission of the Miller Brain Observatory is to enable a new range of neurobiological discoveries by facilitating the transition of advanced, commercially unavailable optical neurotechnologies from proof-of-principle to user-defined, neurobiological problems within one center on campus. This will be achieved by offering a portfolio of advanced brain imaging tools and generating and maintaining a critical mass of long-term optical and computational expertise that would ensure continued innovation, user applications, and management of knowledge. Overview:

The Computational Scientist position presents an ideal opportunity to pursue a career in computational research in an academic research environment. The position offers an opportunity to evaluate, apply, and refine various existing open-source and commercial imaging and neuroimaging analysis software, as well as develop new scalable data processing and analysis pipelines and hardware control solutions.

This position aims at providing computing expertise to the MBO users and collaborators while concurrently deepening the candidate's experience in computational research and software engineering in an academic research environment.

Responsibilities include, but are not limited to the following:

• Improve, adapt, and extend existing data acquisition and processing pipelines and improve existing workflows to efficiently process neuroimaging data;
• Develop efficient data analysis pipelines; maintain streamlined data analysis and storage pipelines;
• Stay up-to-date on open-source software and explore integration into current workflow;
• Generate SOPs and train new MBO users and collaborators;
• Maintain storage and data-processing servers;
• Other duties and special projects as assigned.

REQUIRED QUALIFICATIONS:

• Masters or advanced degree in Computer Science, Computer Engineering, Math, Physics, or a related discipline with a strong emphasis on scientific computing is required
• Must possess strong quantitative/math skills and excellent programming experience in Python, MATLAB, C/C++, Java or similar
• At least 3 years of experience with image processing, data analysis, and visualization
• Strong foundation in computing software, Cloud computing, GPU-accelerated workflows
• Knowledge of good software practices and proficient with version control
• Prior experience with AI/ML, particularly Computer Vision and Deep Learning
• Driven to continually expand knowledge of new scientific software, technological trends, and developments
• Must be comfortable working in a collaborative research environment and able to prioritize multiple project tasks
• Must possess excellent verbal and written communication: ability to convey technical and scientific ideas to experts and non-experts alike
• Excellent ability to provide computational support and advice to academic scientists and researchers

PREFERRED QUALIFICATIONS:

• Ph.D. highly desired
• Prior experience with Ca++ imaging, large-scale 2p/3p microscopy datasets preferred
• Familiarity with optics, microscopy, custom instrumentation, CAD modeling, electronics, robotics, and FPGA programming is a plus

The Rockefeller University does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy, gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service or other non-merit factor. All qualified applicants will receive consideration for employment without regard to the characteristics listed above.

The salary of the finalist selected for this role will be set based on various factors, including but not limited to organizational budgets, qualifications, experience, education, licenses, specialty, and training. The hiring range provided represents The Rockefeller University's good faith and reasonable estimate of the range of possible compensation at the time of posting.