The purpose of this position is to assist NIA Computer Systems Analyst and tasks to include but not limited to:
Collaborate with staff to increase the productivity and efficiency of data analysis using high-performance computing
Develop dedicated and efficient computer software using state-of-the-art computer science approaches, including modern programming languages and coed libraries, machine learning, cross-platform programming, and high-performance computing, including:
Automating data segmentation and data visualization
Developing numerical model simulations and high-performance computing
Performing generating image analysis, including deep convolutional neural networks
Detecting and analyzing subcellular signaling events
Participate in research projects by creating specialized computer software to effective and automatically analyze experimental data, including imaging data generated by different imaging techniques such as high-speed cameras, confocal microscopy, super-resolution SIM imaging, FIB-SEM imaging, MRI imaging as well as data generated by numerical model simulations
Develop customized software to detect and measure specific structures and events of interest for statistical analysis and testing specific hypotheses
Provides technical experience needed to perform analyses, processing and user support of various computer systems using standard statistical procedures and techniques:
Provide project management guidance and assistance to specific projects and activities
Provide statistical support to staff, including data analysis and the generation tools for researchers to use
Interface with staff as needed to gather project requirements, develop project plan with web team, and help execute plan
Support and maintain scientific computer systems including hardware, operating systems, and associated services in a high-performance computing environment
Sets up and uses various computer systems and software packages:
Collaborate with staff to increase the productivity and efficiency of data analysis using high-performance computing
Perform statistical analysis of experimental data obtained at various time points
Determine needs and provide IT support for computerized systems supporting activities
Work with staff on the evolving infrastructure, data engineering pipeline, and data science stacks
Compiles and manages data and assists with quality control:
Perform quality assurance and quality control; troubleshoot issues with image analysis and quality
Analyze results of the data that are collected during experimental studies; perform statistical analysis using statistical analysis software
Produces various reports, graphs and figures for presentation:
Prepare data and analysis for presentations and publication
Prepare research data for presentation and manuscripts
Interface with the statistical programming team to develop programming standards and assist the team with validation and change control documentation
Work products and documents related to increasing the productivity/efficiency of data analysis using high-performance computing. - Ad-Hoc
Work products and documents related to developing dedicated and efficient computer software using state-of-the-art computer science approaches, including modern programming languages and coed libraries, machine learning, cross-platform programming, and high-performance computing. - Ad-Hoc
Work products and documents related to automating data segmentation and data visualization; developing numerical model simulations and high-performance computing; performing/generating image analysis, including deep convolutional neural networks; detecting and analyzing subcellular signaling events. - Ad-Hoc
Work products and documents related to creating specialized computer software to effectively and automatically analyze experimental data, including imaging data generated by different imaging techniques. - Ad-Hoc
Work products and documents related to developing customized software to detect and measure specific structures and events of interest for statistical analysis and testing specific hypotheses. - Ad-Hoc
Clean Equipment - Weekly
Run Validation - Weekly
Inspect Equipment - Weekly
Meet with lab members to present updates - Weekly