Bioinformatician

About the role

Job Summary: The Singapore UCSD RNA Frontier (SURF) program aims to foster strategic collaboration between UCSD and Singapore towards the eventual development of RNA therapeutics. Within this area, SURF projects will involve the development of platforms, modalities or gene targets across multiple disease areas. Leveraging the expertise of Prof. Eugene Yeo, an RNA technologist and renowned expert in RNA biology, SURF will function in close synergy with the UCSD RNA Centre, while complementing and partnering existing national efforts in the RNA space. We are seeking a bioinformatician to lead computational analysis of large-scale datasets generated by SURF projects, as well as spearhead the development, training and refinement of cutting-edge machine-learning models on both internal (in-house) and external data. The role requires expertise across bioinformatics, data science and machine learning, as well as the ability to keep abreast with developments in these fields. In addition, an understanding of biological assays and the underlying biology is necessary to collaborate and advise wet-lab researchers. The successful candidate will contribute across the full data lifecycle - from experimental design and pipeline development to statistical analysis, visualization, and publication - while establishing best practices for data management, reproducibility, and scalability within the SURF program. Duties & Responsibilities: Data Analysis and Computational Modeling Perform analysis of large-scale -omics datasets across and arising from various experimental modalities, such as screening and perturbation data, to assess target modulation, pathway engagement, and potential mechanisms of action. Develop machine-learning models for classification, modeling, inference, or prediction of data modalities (including, but not limited to, gene targets, protein structures, biological pathways or therapeutic small molecules) towards biological discovery or early-stage therapeutic / drug-discovery programs. Pipeline Development & Infrastructure Design, implement, and maintain robust, reproducible bioinformatics pipelines for data processing and analysis. Develop and maintain analysis code in appropriate programming languages (e.g., R, Python, Bash), following software engineering best practices. Contribute to the development and maintenance of computational infrastructure, including use of high-performance computing (HPC) environments or cloud-based platforms. Collaboration Advise wet-lab scientists on experimental design (e.g, power analysis, replication strategies, metadata capture, assay development) to ensure that the data aligns across experimental and analytical goals and is biologically and translationally informative. Participate actively in lab meetings, project reviews, and cross-functional discussions, providing computational insight and guidance. Data Management & Reproducibility Establish and enforce best practices for data organization, version control, documentation, and reproducibility. Develop standardized analysis templates and documentation to support knowledge transfer and long-term sustainability. Ensure compliance with data governance, privacy, and sharing requirements, including preparation of datasets for publication or external collaboration. Publications, Grants & Communication Prepare figures, methods, and results sections for peer-reviewed publications. Present research findings at scientific conferences and stakeholder meetings. Required Education: PhD in bioinformatics, computational biology, data science, machine learning, or a related quantitative discipline, and 5-10 years experience in an academic or industrial research setting. Other Desired Qualifications / Experiences / Skills: Experience working with machine-learning models such as CNNs, RNNs, GANs, GNNs, RAGs and/or transformers, as applied to imaging, sequencing, modeling, inference or prediction of biological datasets. Expertise in scientific programming languages (R, Python) and associated ecosystems. Experience with workflow management systems, containerization (e.g., Docker/Singularity), and HPC or cloud computing. Expertise in RNA biology-related data analysis, particularly involving RNA-sequencing technologies such as scRNA-seq or scATAC-seq. Experience with wet-lab experimental designs involved in high-throughput screening, large-scale mutagenesis or genome-wide CRISPR-based perturbations. Understanding of statistical methods for high-throughput biological data. Experience working closely with wet-lab scientists in a research-driven environment. Track record of co-authorship on peer-reviewed scientific publications.

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