Postdoctoral Scientist – Multimodal Representation Learning for Predictive Biology

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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Post Doc – Data Analytics & Computational Sciences

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Beerse, Antwerp, Belgium

Job Description

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

Johnson & Johnson Innovative Medicine is recruiting for a Postdoctoral Scientist – Multimodal Representation Learning for Predictive Biology to join the Data, Data Science & Artificial Intelligence (DDSAI) organization for a two-year fixed term position, helping advance AI/ML analytics and multimodal modeling for drug discovery. This position will be based at any of the following locations: Cambridge, MA (preferred); Spring House, PA; Beerse, Belgium; or Madrid, Spain. (No fully remote option.)

Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):

USA - Requisition Number: R-094412

Belgium - Requisition Number: R-095646

Spain - Requisition Number: R-095648

Within DDSAI, our teams develop innovative solutions using a variety of data sources across multiple therapeutic areas. We are looking for a highly motivated and innovative Postdoctoral Scientist to work at the intersection of advanced AI/ML modeling at scale, multimodal representation learning, drug discovery and predictive biology. The successful candidate is self-motivated, creative, and an effective communicator, with a strong interest in deep learning for imaging microscopy and multi-omics. The role focuses on developing advanced models that analyze and quantify the heterogeneity of perturbed cellular systems and integrate multi-scale biological data (e.g., high-content imaging, phenomics, transcriptomics, and proteomics) to derive new biological insights that support the next-generation portfolio for drug discovery. A successful candidate should demonstrate a strong capacity to build, evaluate, and benchmark AI/ML methods, and publish findings in top-tier peer-reviewed publications.

Key Responsibilities

• Conceive, design, develop, implement and validate innovative AI/ML solutions for drug discovery problems using multimodal data of perturbed cells.
• Analyze and extract novel biological insights from large-scale, heterogeneous high-dimensional data (e.g., imaging microscopy, phenomics, transcriptomics, proteomics).
• Develop and evaluate innovative computer vision solutions to quantify heterogeneity of perturbed cells.
• Collaborate closely with both internal and external cross-functional teams of scientists and engineers to advance algorithms and product development to improve project outcomes.
• Clearly communicate complex technical methodologies and present findings to diverse audiences and stakeholders to support informed decision-making.
• Follow standard processes for documentation and maintaining an up-to-date code repository.
• Draft manuscripts and disseminate research findings internally and externally (e.g., publishing in peer-reviewed conferences/journals).

Qualifications

• Ph.D. in Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics, or a related major. (PhD completed within the last 3 years or to be completed within the next 6 months.)
• Strong technical expertise in AI/ML for biological applications, with excellent analytical skills in at least one domain area (representation learning, multimodal integration, computational biology, imaging