AI Scientist, Generative AI Models for the Creation of RNA-Based Vaccines

Overview The Genome Institute of Singapore (GIS) is the national flagship for genomic sciences, driving cutting-edge research at the intersection of biology, engineering, and medicine. This position is offered in the Laboratory of AI in Genomics, led by Prof. Mile Sikic, which uses advanced bioinformatics and deep learning approaches to develop next-generation models for genomic data analysis. We are group a of computer scientist with a mission to improve healthcare using advance deep learning models. Located in the heart of Singapore's thriving biomedical hub, GIS offers a dynamic and collaborative environment, with close ties to world-class universities (NUS and NTU), pharmaceutical companies, and biotech start-ups. Joining our team means working on transformative projects with real-world impact, while benefiting from Singapore's vibrant research ecosystem and strong support for innovation. Project background Project background Messenger RNA (mRNA)-based therapeutics, including vaccines, represent a transformative class of drugs for infectious diseases and cancer immunotherapy. Their programmable nature allows rapid adaptation to evolving pathogens and personalized medicine, but effective design of mRNA molecules remains a key bottleneck. Current development relies on trial-and-error methods, leading to long timelines, high costs, and suboptimal outcomes. This project aims to develop an agent-based generative AI system to design both linear and circular mRNA molecules. By unifying the design process into a data-driven, adaptive pipeline, the system will optimize vaccine stability, minimize unwanted immune responses, and accelerate early-stage research and development from months to hours. The outcome will be a robust, scalable platform for creating effective mRNA vaccines for infectious diseases, cancer, and beyond. Job description We are looking for a highly motivated postdoctoral researcher to: Develop generative AI models for mRNA optimization Develop foundation models for mRNA assessment Run large-scale pretraining on high-performance computing infrastructure Perform model finetuning and hyperparameter optimization Evaluate models on experimental data Profile We welcome applications from candidates with: A PhD in computer science, computational biology, applied mathematics, physics, or a related field Proven experience in deep learning research and development Publication record at top-tier AI conferences (e.g., NeurIPS, ICLR, ICML, CVPR, ICCV, ACL, etc)Strong experience in Python programming and solid software engineering skills Experience with biomolecules and/or high-performance computing is a plus Interest in biology, biomolecules, or genomics (prior expertise not required)A structured, independent, proactive and collaborative working style We offer A fully funded position with an internationally competitive salary Professional development opportunities, including support for grant applications and participation in conferences and workshops Access to state-of-the-art research infrastructure, including NSCC?s high-performance computing clusters A dynamic, interdisciplinary, and collaborative research environment The position is initially offered for one year, with the possibility of renewal. How to apply We look forward to receiving your application with the following documents: Letter of Motivation CV Diplomas & Transcripts We accept applications submitted through our online application portal or via email directed to Prof. Sikic at The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified. #J-18808-Ljbffr