Research Associate

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An exciting full-time onsite Research Associate role at DeepIR in Basel, focusing on TCR and pHLA interactions with hands-on work in cell engineering, immune cell characterization, and collaboration with computational scientists. You will design, optimize, and execute workflows for NGS library prep and analysis including HLA-typing and single-cell RNA-seq, generate TCR-engineered cell lines, perform binding and activation assays, and conduct multiparametric flow cytometry while documenting data in Benchling, R, and Prism. The ideal candidate has a Master’s in Immunology, Molecular Biology, or Bioengineering, strong molecular biology and genome editing skills, cell culture experience, familiarity with flow cytometry and immune assays, and solid analytical and communication abilities. To apply, tailor your CV to emphasize relevant project outcomes, demonstrate cross-disciplinary collaboration, and include a concise letter explaining why DeepIR’s data-driven immune recognition resonates with your interests.

We are seeking a Research Associate to join the DeepIR flagship initiative, focusing on cell engineering and the characterization of T-cell receptor (TCR) and peptide-MHC (pHLA) interactions. This role is ideal for a recent master’s graduate with a background in immunology, molecular biology, or bioengineering who wants to contribute to cutting-edge immune engineering research. This is a hands-on experimental role where you will contribute to workflows for TCR discovery, immune cell engineering, and functional characterization of antigen-specific immune responses. In the TCR Discovery subgroup you will be working closely with other experimental and computational scientists.

Tasks

The Research Associate will support the DeepIR research groups through the following duties:

• Next Generation Sequencing: design, optimize, run, and analyze next-generation sequencing library workflows. This may include HLA-typing, TCR/BCR targeted repertoire sequencing, and single-cell RNA-sequencing.
• Cell Engineering: Generate and characterize TCR-engineered cell lines using molecular cloning, gene engineering, and reporter assays.
• Functional Assays: Perform binding and activation assays to assess TCR-pHLA interactions using reporter assays.
• Flow Cytometry: Perform multiparametric flow cytometry analysis to characterize engineered cells and immune responses.
• Experimental Design & Optimization: Develop, refine, and troubleshoot protocols for TCR validation, screening, and characterization to optimize lab workflows.
• Collaboration with Computational Teams: Work closely with bioinformaticians to experimentally validate computational predictions and ensure data is well-structured.
• Data Analysis & Documentation: Maintain detailed, accurate experimental records and analyze data using Benchling, R, and Prism.

Requirements

We are looking for a motivated researcher who meets the following criteria:

• Education: Master’s degree in Immunology, Molecular Biology, Bioengineering, or a related field.
• Molecular Biology: Strong hands-on expertise in molecular biology techniques such as cloning, PCR, plasmid design, and genome editing for cell line engineering
• Cell Culture: Experience with mammalian and or yeast cell culture, transfection, and transduction.
• Immunological Assays: Familiarity with flow cytometry and other assays used for characterizing TCR-pHLA interactions.
• Biological Knowledge: Demonstrated knowledge of immune receptor biology and antigen presentation is preferred.
• Analytical & Communication Skills: Ability to analyze experimental data, troubleshoot protocols, document results in a digital lab notebook, and clearly present findings both verbally and in writing.

About DeepIR

DeepIR is BIIE’s flagship initiative focused on building a deep, mechanistic, and data-driven understanding of immune recognition. The program brings together experimental immunology, protein engineering, and machine learning to generate and integrate large-scale, high-quality datasets that enable predictive models of immune receptor–antigen interactions.