Assoc. Scientist,Post Doc Fellow

Job Description Postdoctoral fellow, AI/ML for Tissue Analytics, Quantitative Biosciences At our company, we are committed to becoming the premier, research-intensive biopharmaceutical company and are dedicated to providing leading innovations and solutions for today and the future. Our Research Scientists serve as the driving force behind our Innovations. We identify and target mechanisms or pathways involved in the disease process, and invent novel treatments/therapeutics to address unmet medical needs. This exceptionally COMPLEX task represents one of the most MULTIDISCIPLINARY endeavours in the field. INNOVATION also thrives at that intersection of multiple disciplines! In this role as a Postdoctoral fellow, AI/ML for Tissue Analytics , you will have the opportunity to work at that intersection of disciplines to shape innovation in biomarker sciences, influence pipeline decisions, and contribute to a research-driven environment with industry-leading expertise. This role is within our Tissue Biomarkers & Analytics team, Quantitative Biosciences, at our state-of-the-art research laboratory in Biopolis, Singapore. If you are PASSIONATE about applying your expertise to address critical unmet needs and accelerate the discovery and development of new drugs, we invite you to join us in pioneering the future of biomarker research for the biopharmaceutical industry. WHAT YOU CAN EXPECT … Opportunity to design, build, validate, and deploy frontier AI/ML and next-generation capabilities for tissue analytics and computational pathology, spanning areas such foundation models, multimodal learning, digital pathology, and emerging AI-enabled biomedical research paradigms. Opportunity to translate novel AI/ML methodologies into scalable solutions for real-world translational research, driving both methodological advances and novel biological insights that accelerate biomedical discovery. Opportunity to collaborate with an interdisciplinary team spanning data science, pathology, biology, translational medicine, and drug discovery, working at the intersection of biology, technology, and AI/data science. Opportunity to grow scientifically and professionally through mentorship from experts across AI/ML, pathology, biomarker sciences, and drug discovery, while building a strong publication record and expanding your international scientific network. WHAT YOU WILL DO … In this role as a Postdoctoral Fellow, AI/ML for Tissue Analytics , you will work at the intersection of AI/ML, pathology, and biomedical research to develop innovative analytical capabilities and apply them to important biomedical and translational research challenges. You will contribute to a research-driven environment with industry-leading expertise while generating both methodological advances and biological insights that can help shape future approaches to biomarker discovery and drug development. A key expectation of this fellowship is the dissemination of scientific advances through peer-reviewed publications and presentations at leading scientific conferences. Your responsibilities will include, but not be limited to: Designing, building, validating, and deploying innovative AI/ML models for tissue analytics and computational pathology, to analyse digital pathology and tissue imaging data from preclinical and clinical studies. Developing and implementing advanced machine learning methodologies, including foundation models, multimodal learning approaches, and other emerging AI techniques, to extract biological insights from complex datasets and advance next-generation tissue analytics capabilities. Formulating scientific hypotheses, designing and executing computational studies, and interpreting AI/ML findings in the context of disease biology, biomarker discovery, and therapeutic development. Evaluating and advancing emerging advances in artificial intelligence, machine learning, and digital pathology, including foundation models and generative AI, identifying opportunities to strengthen internal scientific and analytical capabilities. Collaborating with interdisciplinary teams spanning data science, pathology, biology, and translational research, as well as external collaborators and technology partners where appropriate. Disseminating scientific and technical advances through presentations, technical reports, project proposals, peer-reviewed publications, and scientific conferences. WHAT YOU MUST HAVE … PhD degree in Computer Science, Data Science, Electrical Engineering, Biomedical Engineering, Biomedical Sciences, Computational Biology, Physics, or a related discipline. Track record of scientific publications in peer-reviewed journals and/or presentations at leading scientific conferences, demonstrating the ability to independently drive research from concept through dissemination. Strong background in machine learning and deep learning , including foundation models, representation learning, multimodal learning, generative AI, transformers, self-supervised learning, and related state-of-the art approaches. Experience developing AI/ML models for medical imaging, computational pathology, computer vision, biomedical research, or related domains is highly desirable. Familiarity with emerging AI paradigms, such as agentic AI systems and AI-assisted scientific workflows, would be advantageous. Hands-on experience in scientific programming and machine learning development , including Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Experience with cloud computing, ML engineering, model deployment, and scalable AI workflows using platforms such as AWS (e.g., SageMaker), Databricks, or equivalent environments would be advantageous. Experience with digital pathology, tissue imaging, computational pathology, spatial biology, or related biomedical data modalities. Familiarity with image analysis techniques such as image segmentation, registration, feature extraction, representation learning, and image processing using OpenCV, ITK, SimpleITK, or similar frameworks is highly desirable. Experience with digital pathology platforms and image analysis software, such as HALO, QuPath, Visiopharm, or equivalent platforms would be advantageous. Knowledge of disease biology (e.g., neuroscience, cardiometabolic, immuno