Postdoc, Autonomous Discovery & the AI Life Science Platform (2-year Fixed Term)

Job description

Job Location SINGAPORE TC-BIOPOLIS Job Description About the role What if your science could improve the lives of billions of people, every day, across every stage of life? Skin and scalp biology is one of humanity's most universal and enduring dimensions of health and wellbeing. It accompanies us from infancy through adolescence, adulthood, and healthy aging, shaping how we protect and care for ourselves and our loved ones. The scientific challenges are equally diverse and compelling: supporting skin barrier health in early life, understanding the biological transitions of puberty, enabling superior shaving, grooming, and body care experiences, addressing lifelong scalp disorders such as dandruff, combating hair loss, understanding the biological impacts of menopause, and ultimately unlocking healthier skin and scalp aging throughout longer lifespans. New interventions such as GLP-1 therapies, regenerative medicine approaches, and hair transplantation are further transforming consumer needs and creating unprecedented opportunities for scientific discovery and innovation. The launch of the Skin Bioscience Discovery Accelerator (SBDA) at the P&G Singapore Innovation Center represents a bold commitment to seize this opportunity and shape the future of skin and scalp science. Combining world-class bioscience, advanced experimental systems, multimodal data, computational biology, and AI-enabled discovery, the Accelerator aims to create a fundamentally new engine for innovation in skin and scalp health. This is an invitation to ambitious scientists who want their work to matter beyond the laboratory. You will join a global ecosystem of scientific excellence and work closely with P&G Research Fellows, senior technical leaders, and internationally recognized experts located in Singapore and across P&G's worldwide R&D network. Through mentorship, collaboration, and exposure to some of the company's most accomplished innovators, you will tackle frontier scientific challenges while developing the skills and perspective needed to lead the next generation of discovery. If you are driven by scientific excellence, inspired by large-scale impact, and motivated to tackle some of the most important unanswered questions in human biology, we invite you to help shape the future of skin and scalp health for billions around the world. This 2-year Postdoc position will help define the future of AI-enabled scientific discovery by creating integrated systems where artificial intelligence, biological experimentation, and human expertise operate as a single continuously learning engine. Combining multimodal biological data, predictive models, Design-Build-Test-Learn (DBTL) architectures, and advanced life science platforms, the program aims to establish new frameworks capable of generating hypotheses, guiding experiments, accelerating learning, and building increasingly predictive representations of biological systems. This role is designed for a scientist-builder: someone who can help architect the digital-first discovery engine of the Accelerator, connect AI with experimental biology, and create the continuously learning systems that will power the next generation of skin and scalp bioscience. Key Responsibilities Pioneer new scientific frameworks and discovery approaches to advance understanding of biological performance, resilience, regeneration, and health across the lifespan. Design and execute high-impact research programs leveraging advanced biology, human-relevant experimental systems, multimodal data, and AI-enabled discovery. Work alongside P&G Research Fellows and global scientific leaders to shape scientific strategy and accelerate breakthrough discovery. Operate within a Design-Build-Test-Learn discovery environment, integrating experiments, computational approaches, and emerging technologies to drive continuous learning. Translate breakthrough discoveries into publications, intellectual property, external collaborations, and technology opportunities with global consumer impact. Job Qualifications PhD in Artificial Intelligence, Machine Learning, Computational Biology, Bioinformatics, Computer Science, Computational Life Sciences, Data Science, or a related discipline. Scientists currently completing, or having recently completed, a postdoctoral appointment in a relevant AI-for-Science, computational biology, or autonomous discovery environment are strongly encouraged to apply Demonstrated research excellence through high-quality publications, preprints, patents, open-source contributions, scientific software, or platform development in areas relevant to AI-enabled discovery. Strong expertise in one or more relevant scientific or technical domains, including: AI for Science and biological discovery Machine learning applied to life sciences Multimodal biological data integration Predictive modeling of biological systems Design-Build-Test-Learn architectures Closed-loop experimental learning systems Foundation models, knowledge graphs, or agentic AI for scientific discovery Data architecture for biological research platforms Experience working with complex biological or biomedical data streams, or human-relevant biological model systems. Strong programming, analytical, quantitative, and problem-solving skills, with the ability to translate scientific questions into computational architectures and actionable discovery workflows. Demonstrated ability to work at the interface of AI, biology, experimental science, and platform development. Excellent written and verbal communication skills, including the ability to communicate complex AI and computational concepts to multidisciplinary scientific audiences. Demonstrated ability to work independently while providing technical direction and thought leadership within cross-functional teams. Preferred Qualifications Experience developing AI-enabled discovery platforms in life sciences, pharma, biotech, medtech, AI-for-Science organizations, autonomous lab environments, or frontier research groups. Experience building or deploying Design-Build-Test-Learn systems where experimental results are used to improve predictive models over time. Experience developing computational architectures that connect hypothesis generation, experiment prioritization, data integration, model refinement, and knowledge capture. Experience with multimodal foundation models, biological representation learning, scientific knowledge graphs, large language models for research workflows, or agentic systems for scientific reasoning. Experience applying machine learning to biological datasets. Experience with active learning, causal inference, mechanistic modeling, Bayesian optimization, or other approaches that enable efficient experimental design. Experience integrating internal knowledge, literature, experimental data, imaging, omics, and expert input into unified computational frameworks. Experience developing scalable data pipelines, model evaluation frameworks, reproducible computational workflows, or cloud-based research platforms. Experience collaborating closely with experimental scientists to convert biological questions into testable hypotheses, model-driven experiments, and interpretable outputs. Familiarity with skin, scalp, dermatology, regenerative biology, aging biology, or consumer health science is valuable but not essential if the candidate brings strong experience with biological data and AI-enabled discovery systems. Ideal Candidate Profile We are particularly interested in scientists who: Are motivated by building new discovery systems, not only analyzing existing datasets. Have a strong AI-for-Science mindset and are excited by the opportunity to create a continuously learning biological discovery platform. Thrive in highly interdisciplinary environments spanning artificial intelligence, biology, experimental design, data engineering, and scientific strategy. Are energized by the challenge of