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Postdoc, Computer Vision – AI/ML Radiology | Johnson & Johnson


Job title: Postdoc, Computer Vision – AI/ML Radiology

Company: Johnson & Johnson

Job description: Johnson & Johnson Innovative Medicine (JJIM), a Johnson & Johnson company, is recruiting for a postdoc located in (Cambridge, MA, Titusville, NJ; Raritan, NJ; La Jolla, CA; New York, NY, or Spring House, PA) or Europe (Beerse, Belgium; Zug, Switzerland). Remote work options may be considered on a case-by-case basis and if approved by the Company.JJIM develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiovascular, and infectious disease. Our ultimate goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market – from patients to practitioners and from clinics to hospitals. To learn more about JJIM, one of the Pharmaceutical Companies of Johnson & Johnson, visitThe Data Science Analytics & Insights team within JJIM R&D develops innovative solutions using a variety of data sources across multiple different disease areas. We are looking for an outstanding postdoc with a focus on Computer Vision (AI/ML Radiology).Key Responsibilities:

  • Conceive, develop, and implement analytics and AI/Machine learning radiology solutions to high-priority scientific problems
  • Participate in cross-functional collaborations with external companies and internal scientific, clinical, and data science teams
  • Shape internal/external collaborations and define the scope of research questions
  • Extract insight from complex, unstructured radiology image or other imaging modalities data to improve drug discovery and healthcare (e.g., disease identification, patient stratification, disease insights)
  • Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making

Qualifications

  • Ph.D. degree (grad with in 3 months) in a quantitative discipline (e.g., artificial intelligence, computer science, applied mathematics, electrical engineering, or similar)
  • Demonstrated experience driving research in and applying Computer Vision techniques, such as Transformers, CNNs, Diffusion models, RNNs, GANs, and radiomics.
  • Extensive experience with traditional Computer Vision applications, such as OpenCV, object detection, edge detection, image segmentation
  • Extensive hands-on experience with medical imaging modalities and formats (e.g., DICOM, nrrd, nifti) and collaborating with subject matter experts.
  • Proficiency with one or more programming language such as Python or C++
  • Strong publication record and ability to effectively communicate technical work to a wide audience

Preferred Qualifications:

  • Hands-on technical data analysis, cloud computing (AWS, Azure), dockerization.
  • Experience with radiology imaging (e.g., MRI, CT, PET, Xray, etc.)
  • Experience with different imaging modalities and RWD data
  • Familiarity with and exposure to drug discovery and clinical development processes
  • Experience working closely with healthcare professionals
  • Experience with meaningful disease biology in cancer, immunology, neuroscience, cardiovascular, or infectious disease

Expected Base Salary (US): $95,000 / yrThriving on a diverse company culture and celebrating the uniqueness of our employees, we are committed to inclusion. We are proud to be an equal opportunity employer.Johnson & Johnson is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, or protected veteran status and will not be discriminated against on the basis of disability.Working with Johnson & Johnson can change everything. Including YOU.#JRDDS #JNJDataScience#LI-Remote

Expected salary: $95000 per year

Location: New Brunswick, NJ

Job date: Fri, 23 Aug 2024 07:51:41 GMT

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