Postdoc Position in Computational Antigen Discovery, Utrecht University, Netherlands

Summary

Utrecht University is seeking a highly motivated and experienced Postdoctoral Researcher to join the multidisciplinary research team led by Dr. Fabio Marino within the Biomolecular Mass Spectrometry and Proteomics division (Department of Pharmaceutical Sciences). The role centers on developing and applying state-of-the-art bioinformatics pipelines to discover, quantify, and validate novel immunogenic antigens using mass spectrometry (MS)-based proteomics, immunopeptidomics, and multi-omics data integration to advance vaccine-based cancer immunotherapies.

Designation

• Postdoctoral Researcher (Postdoc)

Overview Table

Research Area

• Computational Biology & Bioinformatics
• MS-based Proteomics & Immunopeptidomics
• Multi-Omics Data Integration (Genomics, Transcriptomics, Ribo-seq, Proteomics)
• Non-canonical HLA-Bound Peptide Discovery & Neoantigen Prediction
• Cancer Immunotherapy & Vaccine Target Discovery

Location

• Utrecht University, Faculty of Science, Department of Pharmaceutical Sciences, Utrecht, The Netherlands.

Eligibility & Qualifications

Required:

• Education: PhD in bioinformatics, computational biology, systems biology, or a closely related computational/quantitative field.
• Technical Experience:
  • Proven experience analyzing mass spectrometry-based immunopeptidomics/proteomics data (DDA, DIA, PRM) with tools such as FragPipe, PEAKS, Spectronaut, MaxQuant, Skyline, or equivalent software.
  • Solid expertise in standard packages and analysis workflows for MS-based immunopeptidomics.
  • Experience with non-canonical HLA-bound peptide discovery pipelines, HLA binding predictors, and in silico immunogenicity tools (e.g., HLA-peptide-TCR binding prediction).
  • Demonstrated ability in multi-modal data integration (combining RNA-seq, Ribo-seq, proteomics, and immunopeptidomics) to prioritize disease-specific targets.
• Programming Skills: Strong proficiency in Python, R, or similar languages.
• Reproducibility: Experience with modern workflow management tools (e.g., Nextflow, Snakemake, CWL).
• Soft Skills: Self-starting attitude, collaborative mindset, and strong written/verbal scientific communication skills.

Preferred:

• Practical experience applying Artificial Intelligence (AI) and Machine Learning (ML) models to multi-omics datasets for target prioritization and biomarker discovery.

Job Description & Key Responsibilities

• Pipeline Development: Design, implement, and maintain scalable, reproducible bioinformatics workflows for large-scale MS-based proteomics, immunopeptidomics, and proteogenomics data.
• Multi-Omics Integration: Merge and analyze complex datasets (genomics, transcriptomics, ribosomal profiling, and immunopeptidomics) in collaboration with external partners (e.g., Princess Máxima Center and Oncode Accelerator).
• Antigen Target Discovery: Identify, quantify, and prioritize tumor-specific and non-canonical HLA antigens for vaccine development and immunotherapy applications.
• Scientific Output & Dissemination: Contribute actively to high-impact peer-reviewed publications, national/international conferences, and research grant applications.
• Team & Infrastructure Support: Provide data analysis, visualization, and bioinformatics support to team members and maintain institutional computational infrastructure.

How to Apply

1. Click the “Apply now” button directly on the Utrecht University Vacancy Portal.
2. Submit the following required documents:
   • A comprehensive Curriculum Vitae (CV).
   • A Letter of Motivation detailing your relevant research experience and fit for the role.
   • Contact details (names, email addresses, and phone numbers) of at least two academic/professional references.

Inquiries:

• Position Details: Celine Miedema (t.c.miedema@uu.nl)
• Application Procedure: Science Recruitment Team (science.recruitment@uu.nl)

Last Date for Apply

• 31 August 2026(Note: Applications are evaluated on a rolling basis, and the position may close earlier if a suitable candidate is identified).

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