SCIENTIST, COMPUTATIONAL BIOLOGY
- Employer
- R&D Partners
- Location
- Cambridge Metro Area, Massachusetts, US
- Salary
- Competitive
- Closing date
- Jul 8, 2021
View more
- Sector
- Academic / Research
- Field
- Informatics / GIS
- Discipline
- Biology
- Salary Type
- Salary
- Employment Type
- Full time
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Position Summary:
Maintain and improve proteomics data processing pipelines
Preform the quality-control (normalization/imputation/filtering) and analysis of proteomics data (secondary and tertiary analyses)
Document workflows and ensure data injection, versioning control and curation using in-house built data warehouse
Present results and conclusions to internal and external stakeholders
Requirements:
M.Sc or Ph.D. in Bioinformatics, Computer Science, Molecular Cell Biology, Biochemistry, Analytical Chemistry or related fields. Minimum 1-3 years research experience (academics/industry)
Experience in processing proteomics data and understanding of the complexity of proteomics data structure is a must.
Strong experience in multivariate, pathway and network-based analyses.
Fluency in R, Bioconductor and data visualization tools (ggplots, plotly)
Familiarity with proteomics data pipelines for data-dependent and independent acquisitions and data types from MaxQuant, Fragger, DIA_NN, Spectranaut, Peaks workflow.
Maintain and improve proteomics data processing pipelines
Preform the quality-control (normalization/imputation/filtering) and analysis of proteomics data (secondary and tertiary analyses)
Document workflows and ensure data injection, versioning control and curation using in-house built data warehouse
Present results and conclusions to internal and external stakeholders
Requirements:
M.Sc or Ph.D. in Bioinformatics, Computer Science, Molecular Cell Biology, Biochemistry, Analytical Chemistry or related fields. Minimum 1-3 years research experience (academics/industry)
Experience in processing proteomics data and understanding of the complexity of proteomics data structure is a must.
Strong experience in multivariate, pathway and network-based analyses.
Fluency in R, Bioconductor and data visualization tools (ggplots, plotly)
Familiarity with proteomics data pipelines for data-dependent and independent acquisitions and data types from MaxQuant, Fragger, DIA_NN, Spectranaut, Peaks workflow.
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