Johnson & Johnson Innovative Medicine
Postdoctoral Scientist, In Silico Immune Disease Models (m/f/x)
Johnson & Johnson Innovative Medicine, Spring House, Pennsylvania, United States, 19477
Postdoctoral Scientist, In Silico Immune Disease Models
Johnson & Johnson Innovative Medicine is seeking a Postdoctoral Scientist, In Silico Immune Disease Models with a strong preference for this individual to be located at one of our sites in Berlin, Germany, Beerse, Belgium, Spring House, PA, Cambridge, MA, Titusville, NJ, or La Jolla, CA. Key Responsibilities In this role, you will join the Data Science and Digital Health organization within Johnson and Johnson Innovative Medicine, focusing on leading the development of advanced network-based in silico immune/autoimmune disease models. You will play a crucial role in identifying, characterizing and advancing therapeutic candidates for clinical development by providing innovative, multi-modal insights of complex biological regulation through the lens of computational modeling. Lead collaborations with Translational Research, Discovery, and Development teams to develop AI/ML models that drive insights for personalized medicine. Analyze and integrate high-dimensional biological data from spatial transcriptomics and single-cell sequencing for model training and validation. Develop and implement scalable statistical and machine learning algorithms using large-scale transcriptomics datasets. Oversee the design of experiments that facilitate rigorous model evaluation in collaboration with cross-functional teams. Ensure compliance with internal coding standards, data integrity practices, and reproducibility protocols for research outputs. Communicate model performance and research findings effectively through technical reports, presentations, and contributions to peer-reviewed publications. Mentor junior researchers and provide technical leadership to enhance team capabilities and research output. Qualifications A recent Ph.D. degree within three years in computer science, artificial intelligence, software engineering, or a related field. Proficient in Python and C/C++. Demonstrated ability to design and implement reproducible workflows for large scale transcriptomics data analysis. Strong background in developing and optimizing machine learning algorithms. Knowledge graphs or ML-driven biological network modeling strongly preferred. Applying such algorithms and methods in a biological context. Molecular and immunological contexts strongly preferred. Proven track record of impactful research with publications in top-tier peer-reviewed journals. Experience in defining research strategies and setting project milestones to drive innovation in foundational models. Strong communication skills to present complex concepts clearly to both scientific peers and non-experts. Preferred Qualifications First-hand experience in oncology and/or immunology research. Knowledge of therapeutic strategies and clinical implications in cancer and immune-related diseases. Familiarity with multi-omics models and integrating data across multiple platforms.
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Johnson & Johnson Innovative Medicine is seeking a Postdoctoral Scientist, In Silico Immune Disease Models with a strong preference for this individual to be located at one of our sites in Berlin, Germany, Beerse, Belgium, Spring House, PA, Cambridge, MA, Titusville, NJ, or La Jolla, CA. Key Responsibilities In this role, you will join the Data Science and Digital Health organization within Johnson and Johnson Innovative Medicine, focusing on leading the development of advanced network-based in silico immune/autoimmune disease models. You will play a crucial role in identifying, characterizing and advancing therapeutic candidates for clinical development by providing innovative, multi-modal insights of complex biological regulation through the lens of computational modeling. Lead collaborations with Translational Research, Discovery, and Development teams to develop AI/ML models that drive insights for personalized medicine. Analyze and integrate high-dimensional biological data from spatial transcriptomics and single-cell sequencing for model training and validation. Develop and implement scalable statistical and machine learning algorithms using large-scale transcriptomics datasets. Oversee the design of experiments that facilitate rigorous model evaluation in collaboration with cross-functional teams. Ensure compliance with internal coding standards, data integrity practices, and reproducibility protocols for research outputs. Communicate model performance and research findings effectively through technical reports, presentations, and contributions to peer-reviewed publications. Mentor junior researchers and provide technical leadership to enhance team capabilities and research output. Qualifications A recent Ph.D. degree within three years in computer science, artificial intelligence, software engineering, or a related field. Proficient in Python and C/C++. Demonstrated ability to design and implement reproducible workflows for large scale transcriptomics data analysis. Strong background in developing and optimizing machine learning algorithms. Knowledge graphs or ML-driven biological network modeling strongly preferred. Applying such algorithms and methods in a biological context. Molecular and immunological contexts strongly preferred. Proven track record of impactful research with publications in top-tier peer-reviewed journals. Experience in defining research strategies and setting project milestones to drive innovation in foundational models. Strong communication skills to present complex concepts clearly to both scientific peers and non-experts. Preferred Qualifications First-hand experience in oncology and/or immunology research. Knowledge of therapeutic strategies and clinical implications in cancer and immune-related diseases. Familiarity with multi-omics models and integrating data across multiple platforms.
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