The Preclinical Imaging Core (PIC) at the Center for Advanced Brain Imaging and Neurophysiology (CABIN) of University of Rochester Medical center seeks an enthusiastic Data Analyst to join their team to support the core with multimodal MRI data analysis.
Requirements
- Matlab, image processing software (SPM, MRIstudio, MRtrix)
- statistical methods
- Programming (MATLAB/Python/R), and mathematical/data analysis.
- Experience with small animal research, medical image analysis, and MRI data analysis.
- Prior experience with MRI scanner usage, especially Bruker systems
- Experience with designing and implementing new customized databases and data pipelines
- Expertise in the long-term management of large multimodal imaging datasets with special emphasis on data cleaning, organization, and quality control.
Responsibilities
- Analyze preclinical data: Focus on analyzing preclinical MRI data from different labs using Matlab, image processing software (SPM, MRIstudio, MRtrix) and using statistical methods to analyze the data, identify trends, and draw conclusions.
- Develop data analysis methods: Design and implement analytical approaches to extract meaningful insights from the data.
- Generate reports: Prepare reports and presentations summarizing findings and insights from data analysis.
- Collaborate with researchers: Work closely with scientists and researchers to understand data requirements and ensure alignment with research objectives.
Other
- The successful candidate will be working under the supervision of the PIC director and supporting the core with multimodal MRI data analysis.
- It is a full-time and onsite position requiring the candidate to deal with multiple labs and to work as a team.
- The successful candidate is expected to follow the institutional guidelines for preclinical research, be very detail-oriented, organized, and motivated to learn new skills in small animal MRI/PET.
- Must be highly organized, self-motivated individual capable of working independently in a collaborative setting.
- Other duties as assigned.
