Summer Intern
Monday 26th February 2024
Contact Email for the Job Posting [email protected]
Organization Janssen R&D, Johnson & Johnson
Location Cambridge (remote option available)
Title Summer Intern
URL https://jobs.jnj.com/en/jobs/2406167569w/intern-ph-d-digital-pathology-imaging/
Closing date Mar 15, 2024
Description At Johnson & Johnson, we use technology and the power of teamwork to discover new ways to prevent and overcome the world’s the most significant healthcare challenges. Our Corporate, Consumer Health, Medical Devices, and Pharmaceutical teams leverage data, real-world insights, and creative minds to make life-changing healthcare products and medicines. We're disrupting outdated healthcare ecosystems and infusing them with transformative ideas to help people thrive throughout every stage of their lives. With a reach of more than a billion people every day, there’s no limit to the impact you can make here. Are you ready to reimagine healthcare?
The primary objective is to establish a robust digital pathology analysis pipeline, focusing on the processing of diversely stained imaging data, including H&E, mIF, and IMC. The pipeline will incorporate foundational modules for cell/tissue segmentation and cell phenotyping to extract clinically meaningful insights into tumor micro - environment such as tumor immune phenotype (TIP) and tertiary lymphoid structures (TLS). The intern's pivotal role in this project involves leveraging our internal repository of multi-modality datasets and independent tool suites. Through this, the candidate will actively engage in the design, optimization, and validation of the pipeline.
What success would look like:
Achieve a good understanding of existing methodologies of related frameworks through summarization, presentation, and internal discussion.
Define appropriate target performance metrics both objective (dice, recall rate, etc.) and subjective (visualization, multi-modality overlay)
Deliver and deploy working PoCs for TIP and TLS analysis
Define, lead, and leverage internal/external annotation effort for initial validation.
Effectively communicating results and findings within the organization through internal meetings and presentations.
Draft manuscripts to be used for potential internal & external publications.
Preferred Qualifications:
Must be pursuing a Ph.D. in the domain of Biomedical Engineering, Computer Science, Computational Pathology or related field
Strong background in computer vision, deep learning, and/or machine learning.
Strong experience in digital pathology imaging (H&E, mIF and/or MC) analysis and its application in oncology is strongly preferred.
Strong implementation experience with high level languages (Python) and frameworks (PyTorch or TensorFlow), data science and familiarity with machine learning libraries (e.g., NumPy, SciPy, pandas, scikit-learn, Cell profiler, PathML, ilastik, DeepCell etc.).
Excellent verbal and written communication and presentation skills.
Able to work and collaborate with diverse teams (imaging and biomarkers)
Familiarity with or willingness to learn best practices for code development and sharing.
Apply here: https://jobs.jnj.com/en/jobs/2406167569w/intern-ph-d-digital-pathology-imaging/
contact email: [email protected]