In order to understand how brains work we need to map brain circuits. The classical discipline of neuroanatomy, where a central task is to understand circuit connectivity in brains, has undergone a renaissance and rapid growth due to our ability to acquire and analyze whole-brain image data volumes. Our laboratory originally proposed (https://doi.org/10.1371/journal.pcbi.1000334) and is carrying out whole-brain connectivity mapping in the mouse and the marmoset at a mesoscopic scale. We employ an integrated research effort including a high throughput experimental pipeline, analysis of the resulting big data volumes using machine learning methods, and dissemination on the web (http://mouse.brainarchitecture.org/).
As part of in the Brain Initiative Cell Census network, which aims to create a cell-type atlas of the mouse brain, we are engaged in management, analysis and dissemination of cell-type specific data sets to manage, analyze and disseminate whole-brain data sets for mouse pertaining to brain-wide distributions of specific cell types, reconstruction of single neurons projecting across the brain, and single-cell/single-nucleus transcriptome data.
We are recruiting for multiple open positions; application deadline: ASAP
We are currently seeking a Data engineer/analyst with expertise in big image data and a background in machine learning to work on a petabyte+ dataset of histological brain image volumes. A successful candidate should be comfortable working in a Linux environment and distributed/networked computation, and be able to participate in maintaining and growing a large storage and compute cluster.
The individual is expected to be able to build efficient, flexible, extensible, and scalable solutions to system administration problems and big data handling.
Develop and translate algorithms (image processing) that integrate into working prototype code.
Create algorithms/heuristics to extract information from large data sets and implement into software/scripts.
Maintain and enhance data pipeline (image handling, cluster) for scalability and reliability.
Mine and organize data sets of both structured and unstructured data.
Design, implement, and support a platform to provide ad-hoc access to large image datasets.
Develop interactive dashboards, reports, and analysis templates.
This is a salaried position, with benefits. Compensation will be set DOE. Applicant must be willing to relocate to Cold Spring Harbor Labs in Long Island NY.
The Mitra laboratory at CSHL combines experimental, computational and theoretical approaches to understand how brains work.
A MS or PhD degree in Computer or Data Science, Machine Vision, Artificial Intelligence, Machine Learning, or related technical field (Mathematics/Statistics, physical science/ engineering strongly desired).
Linux and software development skills are required together with experience coding in C/C++, Python and associated languages. (MATLAB experience is desirable.)
Database engineering and coding skills are required, including those for big-data (MySQL/NoSQL, etc).
Experience with the software stack/framework relevant for distributed processing of big data is required (e.g. Spark, SGE)
Experience in building or maintaining, and interacting with, large, scalable, or high-performance computer systems is required. (GPU coding & Production backup processes a plus)
Clustering of OS/Applications, and understanding points of scalability
Cold Spring Harbor Laboratory (CSHL), founded in 1890, is a preeminent international research institution, achieving breakthroughs in molecular biology and genetics and enhancing scientific knowledge worldwide.
The institution consists of over 600 researchers and technicians, with expertise in cancer, neuroscience, quantitative biology, plant biology, bioinformatics & genomics. CSHL has collaborations with top clinical institutions including Memorial Sloan-Kettering, Dana-Farber, Johns Hopkins, NYU, Weill Cornell, Columbia University, Yale and UCLA. 50% of our research funding is from private and unrestricted sources, allowing a unique degree of scientific freedom and collaboration.