Ridgefield, CT Computational Antibody Repertoire Analysis Co Op Unit

Job description

Description Are you passionate about computational immunology and applying immune repertoire science to improve biotherapeutic discovery efficiency? As a Computational Antibody Repertoire Analysis Co-Op in Biotherapeutics Research, you will actively participate in an ambitious project that will build a state-of-the-art NGS computational pipeline that will enable prioritization of antibody hits to decrease non-binder and low affinity binder attrition. You will learn to use somatic hypermutation models to infer B cell evolutionary trajectories and predict the most evolved clones within clonotype families from immune repertoires deriving from BI’s humanized mouse model. You will work with BI scientists to benchmark computational models and help develop a reproducible pipeline that enriches for likely antigen binders and ranks higher-affinity candidates within expanded clonotype families. This role offers hands-on experience with single-cell and bulk immune-repertoire data, antibody lineage analysis, model evaluation, scientific software development, and translation of computational results into experimentally testable hypotheses. As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunities for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the company’s success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, diversity and inclusion, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim's high regard for our employees. Duties & Responsibilities • Curate, quality-control, and analyze antibody repertoire sequencing datasets generated using platforms such as 10x Genomics and SMART-seq/BCR-seq. • Benchmark somatic hypermutation models and related computational methods using predefined training and held-out test datasets. • Evaluate model calibration, predictive performance, uncertainty, and generalization across experimental platforms, immunization conditions, and mouse background strains. • Apply immune-repertoire analysis tools, including components of the Immcantation framework such as Shazam and Dowser, to characterize clonotypes, lineage relationships, mutation patterns, and selection signatures. • Contribute to an end-to-end sequence-prioritization workflow that ranks likely antigen binders and higher-affinity variants within clonotype families. • Explore whether SHM-based features can be complemented by protein language models or other machine-learning approaches. • Develop reproducible analyses using version-controlled code, documented software environments, configuration files, test datasets, and quality-control reports. • Collaborate with computational and laboratory scientists to define validation datasets and compare computational rankings with binding and affinity measurements. • Summarize results, limitations, and recommendations in clear presentations, technical documentation, and a final project report. Requirements Must be a current undergraduate, graduate or advanced degree student in good academic standing. Student must be enrolled at an accredited college or university for the duration of the co-op. Overall cumulative minimum GPA from last completed quarter/semester 3.0 GPA (on a 4.0 scale) preferred. Major or minor in related field of co-op. Undergraduate students must have completed at least 12 credit hours at current college or university. Graduate and advanced degree students must have completed at least 9 credit hours at current college or university. Desired Skills, Experience and Abilities: