Spring Co-Op, Oncology Research In Vivo Discovery

Location: Ridgefield, CT, Unit, Co

Spring Co-Op, Oncology Research In Vivo Discovery

Description

Boehringer Ingelheim is currently seeking a talented and innovative Co-Op student in Spatial Biology to join our Oncology Research – Oncology Pharmacology and Disease Modeling team located at our Ridgefield, CT facility.

The Co-Op will contribute to both the experimental generation and computational analysis of spatial biology data to characterize preclinical oncology models and their relationship to human disease. The project will combine spatial transcriptomics, multiplex tissue imaging, image analysis, and cross-species approaches to investigate cellular composition, molecular states, and spatial organization within the tumor microenvironment.

This internship is ideally suited for a PhD student interested in working at the interface of tumor biology, spatial technologies, quantitative imaging, and computational analysis.

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 opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies´ 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, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim´s high regard for ouremployees.

Duties & Responsibilities

Perform spatial biology experiments using human and mouse tissues, including staining, assay execution, imaging, and experimental QC.

Support optimization and implementation of spatial transcriptomic and high-plex protein imaging workflows, including tissue- and assay-specific optimization.

Analyze spatial transcriptomic and multiplex imaging datasets, including data QC, visualization, cell/tissue annotation, segmentation, phenotyping, and spatial analysis.

Integrate spatial RNA, protein imaging, and histopathology data to characterize tissue architecture, cellular phenotypes, and spatial relationships.

Apply cross-species approaches to compare mouse tumor models with human disease and identify conserved and divergent biological features.

Collaborate with scientists across in vivo pharmacology, pathology, translational research, spatial biology, and computational sciences to interpret results and communicate findings.

Requirements

Must be a current 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.

Eligibility Requirements

Must be legally authorized to work in the United States without restriction.

Must be willing to take a drug test and post-offer physical (if required).

Must be 18 years of age or older.

Compensation Data

This position offers an hourly rate of $24.00/hr - $33.00/hr. commensurate with the level of degree program in which an applicant is actively enrolled. We continuously review market data and may adjust salary ranges as needed in the future. Benefits include medical coverage, paid time off, paid holidays, disability and life insurance benefits, and other company-sponsored benefits. For additional information regarding our benefits and Total Rewards offerings, please click here.

Desired Skills, Experience and Abilities

Experience analyzing single-cell and/or spatial transcriptomic data using R or Python.

Experience with or strong interest in digital pathology, multiplex imaging, quantitative image analysis, or spatial statistics.

Familiarity with computational tools or frameworks for single-cell/spatial analysis and image analysis.

Strong interest in tumor microenvironment biology and cross-species modeling.

Ability to critically interpret complex biological datasets and communicate findings to both computational and experimental scientists.