Rockefeller University Computational Scientist | Rockefeller Brain Observatory in New York, New York
Neurobiological discoveries have been fueled in recent years by rapid development of optical imaging tools. In particular, the Laboratory of Neurotechnology and Biophysics led by Professor Alipasha Vaziri at The Rockefeller University has developed a wide portfolio of such advanced neurotechnologies that allow for large-scale and whole-brain optical recording and manipulation of neuroactivity at high spatiotemporal resolution across model systems. As such, there is major untapped potential for biological impact by making these and other commercially-yet-unavailable neurotechnologies more broadly available to the biological user community at The Rockefeller University and beyond. Enabled in part by generous support of the Kavli Foundation, the Kavli Neural Systems Institute leadership and the university aim to accomplish this goal by establishing a new center, the Rockefeller Brain Observatory (RBO). The core mission of the RBO is to maximize the biological impact of optical neurotechnologies by facilitating the transition of advanced commercially unavailable optical neurotechnologies from proof-of-principle to user-defined, neurobiological questions. This will be done by offering a portfolio of advanced brain imaging tools; generating and maintaining a critical mass of long-term optical and computational expertise that will ensure continued innovation, continuity, and management of knowledge; and extended support of the user base.
To further the mission of the RBO, the university is seeking a candidate for the role of a Computational Scientist at the RBO. The Computational Scientist position presents an ideal opportunity to pursue a career in computational research or software engineering at a highly collaborative 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. To facilitate this, the Computational Scientist interacts with the wider community of open-source scientific software developers, and contributes by developing and deploying software to acquire, analyze, process, and archive a variety of neuroimaging data generated by the RBO. In this role, the candidate works closely with collaborating Rockefeller laboratories to identify needs and map them with: (1) existing software solutions for the current RBO imaging technologies, and (2) hardware infrastructure such as the center for high performance computing. Working closely with the imaging experts at the RBO, the candidate will have the opportunity to assemble and maintain various computational resources including high performance data acquisition workstations, CPU/GPU analysis workstations, and storage solutions. This position aims at providing computing expertise to the RBO users and collaborators, while concurrently deepening the candidate's experience in computational research and software engineering in a collaborative 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 new data analysis pipelines
Maintain streamlined data analysis and storage pipelines
Keep abreast of relevant open-source software and explore avenues to integrate into current workflow
Generate SOPs and train new RBO users and collaborators
A degree in Computer Science, Computer Engineering, Physics, Electrical Engineering, Neuroscience or a related field, with strong scientific computing emphasis required (Masters or PhD preferred)
Strong quantitative/math skills and excellent programming experience in C/C++ or Java, Python, MATLAB, Julia
Experience with image analysis and data processing with a strong emphasis on popular scientific computing libraries (eg., SciPy, NumPy, Dask, ImgLib2), extensible applications (eg., Fiji, BigDataViewer, Napari, Neuroglancer), performant data formats (eg., HDF5, N5, Zarr)
Strong foundation in computing software and hardware, CPU/GPU/Cloud-based HPC offerings, GPU-accelerated workflows
Knowledge of good software practices and proficient with version control (Git)
Familiarity with Machine Learning, particularly Computer Vision and one or more Deep Learning frameworks (PyTorch, TensorFlow, JAX, or similar)
Driven to continually expand knowledge of new scientific software, technological trends, and developments
Comfortable working in a collaborative research environment and able to prioritize multiple project tasks
Excellent verbal and written communication: ability to convey technical and scientific ideas to experts and non-experts alike
Ability to provide computational support and advice to academic scientists and researchers
Prior experience with large microscopy or imaging datasets (2-photon, light-sheet, light-field etc.) is preferred.
Familiarity with optics, microscopy, optical system designs for neuroscience or life science research is a plus.
Ability to create mechanical designs using 3D CAD modeling software (e.g., AutoDesk Inventor) is a plus.
Knowledge of standard laboratory electro-mechanical systems (eg., galvos, servos, piezos, etc.) and test automation software (eg., LabVIEW, Python, etc.) is a plus.
Knowledge of hardware control software and FPGA programming is a plus.
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.
Compensation Range: Min
Compensation Range: Max
Job Locations US-NY-New York
Position Type Regular Full-Time