Bioinformatician

BERKELEY EDUCATION ALLIANCE FOR RESEARCH IN SINGAPORE LIMITED; Singapore; In loco; SGD 90.000 - 140.000; Tempo pieno

Descrizione del lavoro

The Singapore UCSD RNA Frontier (SURF) program seeks a data scientist to analyze multi-omics data and develop ML models to advance RNA therapeutics. You will design and optimize pipelines, collaborate with wet-lab scientists, and contribute to reproducible data governance and publications.

This role demands strong programming skills, HPC or cloud experience, and a drive for translational biology. Candidates should hold a PhD in a quantitative field and have 5–10 years in research settings.

Competenze

• PhD in bioinformatics or related field with 5–10 years in academic or industrial research.
• Experience with machine-learning models applied to biological data is essential.

Mansioni

• Analyze large-scale -omics datasets to assess target modulation, pathways, and action mechanisms.
• Develop robust, reproducible bioinformatics pipelines using R, Python, and Bash.

Conoscenze

Data analysis

Computational biology

Machine learning

Python

R

Formazione

PhD in bioinformatics or related field

Strumenti

Docker

Singularity

HPC

Cloud computing

R

Python

Workflow management systems

Descrizione del lavoro

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.

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.