Computational Biology Post Doctorate Fellow, Biomarker Department
Scorpion Therapeutics, Carlsbad, California, United States, 92002
Role Summary
Computational Biology Post Doctorate Fellow in the Biomarker Department. Join the Biomarker group to develop, support, and implement the biomarker strategy for Ionis’ pipeline, focusing on proteomic and EEG data to identify biomarkers for neurology programs. Responsibilities include aggregating and harmonizing datasets, building analysis pipelines in R and Python, and validating findings in cohorts and targeted assays. Strong computational biology background and experience with multimodal omics data are required; excellent cross-functional communication is essential. Responsibilities
Access, harmonize, and analyze proteomics and EEG data in collaboration with the Biomarker group and external partners Independently design and deploy computational pipelines to process and analyze proteomics and EEG data Communicate project status, timelines, and analysis updates effectively with team members Integrate findings from omics datasets to support Ionis programs Participate in collaborations to access and contribute to additional datasets for related projects Qualifications
Required: PhD in computational biology or a related field Preferred: Degree and/or background in neurological diseases and/or biomarker discovery; experience handling EEG data Required: Critical-thinking and problem-solving skills with attention to detail Required: Experience designing and executing scientific experiments and projects Required: Experience working with multi-modal omics datasets and large proteomic datasets (e.g., O-link, untargeted LC-MS data), evidenced by high-impact publications Required: Proficiency in R, Python, or similar languages for analysis of processed proteomics data, including data wrangling, QC, normalization, and differential protein abundance testing Preferred: Experience with tidyverse, MSstats, DEP2, pandas, numpy, scikit-learn, and pathway/network analysis tools (e.g., GSEA/fgsea, clusterProfiler) Beneficial but not essential: Running targeted protein ELISAs to validate computational findings Education
PhD in computational biology or a closely related field Additional Requirements
Candidate must be in San Diego or willing to relocate (onsite or hybrid) Travel minimal 0–10%
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Computational Biology Post Doctorate Fellow in the Biomarker Department. Join the Biomarker group to develop, support, and implement the biomarker strategy for Ionis’ pipeline, focusing on proteomic and EEG data to identify biomarkers for neurology programs. Responsibilities include aggregating and harmonizing datasets, building analysis pipelines in R and Python, and validating findings in cohorts and targeted assays. Strong computational biology background and experience with multimodal omics data are required; excellent cross-functional communication is essential. Responsibilities
Access, harmonize, and analyze proteomics and EEG data in collaboration with the Biomarker group and external partners Independently design and deploy computational pipelines to process and analyze proteomics and EEG data Communicate project status, timelines, and analysis updates effectively with team members Integrate findings from omics datasets to support Ionis programs Participate in collaborations to access and contribute to additional datasets for related projects Qualifications
Required: PhD in computational biology or a related field Preferred: Degree and/or background in neurological diseases and/or biomarker discovery; experience handling EEG data Required: Critical-thinking and problem-solving skills with attention to detail Required: Experience designing and executing scientific experiments and projects Required: Experience working with multi-modal omics datasets and large proteomic datasets (e.g., O-link, untargeted LC-MS data), evidenced by high-impact publications Required: Proficiency in R, Python, or similar languages for analysis of processed proteomics data, including data wrangling, QC, normalization, and differential protein abundance testing Preferred: Experience with tidyverse, MSstats, DEP2, pandas, numpy, scikit-learn, and pathway/network analysis tools (e.g., GSEA/fgsea, clusterProfiler) Beneficial but not essential: Running targeted protein ELISAs to validate computational findings Education
PhD in computational biology or a closely related field Additional Requirements
Candidate must be in San Diego or willing to relocate (onsite or hybrid) Travel minimal 0–10%
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