At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.
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**Job Function: **
Career Programs
**Job Sub Function: **
Post Doc – Data Analytics & Computational Sciences
Job Category:
Career Program
All Job Posting Locations:
Barcelona, Spain
Job Description:
Job Description
**About Innovative Medicine **
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Position Overview - Tasks & Responsibilities:
The Computer Vision team at Johnson & Johnson Innovative Medicine is seeking an outstanding postdoctoral researcher for application of advanced AI/ML methods to microscopic images of clinical and preclinical histopathology. The ideal candidate is knowledgeable, self-motivated, and creatively inclined with strong interest and extensive experience in computer vision, deep learning, and medical image analysis. The position will focus on application and refinement of J&J’s deep learning algorithm portfolio to quantitate histopathology, in addition to implementation of novel state of the art techniques. These innovative technologies improve our understanding of cellular organization and tissue structure, ultimately enhancing clinical trial efficacy and efficiency. This position offers the opportunity to work within a cross-functional team in a pharmaceutical environment. In addition to gaining technical exposure through the various projects the team is undertaking, you will also gain valuable insights into how robust and scalable algorithms are developed to support clinical trials, under the mentorship of experienced scientists, while contributing to initiatives aimed at improving global health.
**Key Responsibilities: **
Conceive, design, develop, implement and validate innovative computer vision solutions addressing high-priority challenges in microscopy.
Collaborate closely both with internal and external cross-functional teams of scientists, engineers, and clinicians, to advance algorithm and product development to improve project outcomes.
Shape internal and external collaborations and define scope of research questions
Clearly communicate complex technical methodologies and present findings to diverse audiences and stakeholders to support informed decision-making.
Extract insight from complex, unstructured microscopy images to improve drug discovery and healthcare (e.g., disease identification, patient stratification, disease insights, therapy response).
Prepare and draft manuscripts outlining development methodologies to contribute to the scientific community.
**Qualifications **
Ph.D. degree completed with in 2-3yrs, or Ph.D. candidate with a confirmed defense date within 3 - 4 months. in a field related to deep learning, such as computer science, physics, mathematics, applied mathematics, computer science/artificial intelligence, or related engineering disciplines. Equivalent experience will also be considered.
Strong publication record with the ability to effectively communicate technical work to a broad audience. Publications in conferences such as CVPR, ICLR, ICCV, ICML, MICCAI, ACL, and NEURIPS are preferred but not mandatory.
Demonstrated deep experience researching and applying state-of-the-art computer vision techniques (e.g., Transformers, CNNs, RNNs, GANs, foundation models /self-supervision, panoptic segmentation, weakly supervised learning, few/zero-shot learning, etc)
Demonstrated experience in classical machine learning methods such as random forests, SVMs, etc.
Extensive experience applying, extending, and optimizing traditional image processing mathematics and methods (edge detection, thresholding, color transformations, morphological processing, etc)
Experience with microscopy images such as histopathology, confocal, or fluorescence microscopy.
Strong proficiency in Python and a related deep learning library (tensorflow, pytorch).
Fluent in English.
Strong communication & interpersonal skills.
Ability to work in a matrix organization and participate effectively both independently and as part of multiple teams.
Capacity to work under strict timelines.
Preferred Experience
Experience with multimodal AI.
Familiarity with drug discovery and clinical development processes.
Experience working closely with healthcare professionals.
Experience with meaningful disease biology in oncology, immunology, neuroscience, cardiovascular, or infectious disease.
Johnson & Johnson is an 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, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson and Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, please email the Employee Health Support Center (ra-employeehealthsup@its.jnj.com) or contact AskGS to be directed to your accommodation resource.
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**Required Skills: **
Preferred Skills: