Description

About the Job

ATOMIC is a new generation of AI-enabled biological discovery platforms. The goal is to connect experimental data generation, automation, computational analysis, and human expertise into a practical “lab-in-a-loop” system that can accelerate how we design, analyse, and learn from biological experiments.

A major bottleneck in AI-driven biomedical research, including drug development programs, is the lack of high-quality, scalable, and biologically validated data. ATOMIC aims to address this by generating and analysing large-scale omics datasets, including perturbation, transcriptomic, and single-cell datasets, to better understand biological mechanisms, drug resistance, toxicity, and disease-relevant cellular states.

We are looking for a motivated Computational Biology Research Associate (RA) to support the computational analysis and interpretation of biological datasets as part of this effort. The role is well suited for someone interested in applying bioinformatics, data science, and AI/Agentic methods to real-world biomedical research questions.

Job Description

This role requires an individual interested in applying computational and AI methods to biological data analysis. The RA will support the analysis and interpretation of large-scale biological datasets, including bulk and single-cell transcriptomics, with a focus on understanding biological mechanisms and generating hypotheses from omics data.

The work will involve developing, applying, and maintaining computational pipelines for bioinformatics analysis, integrating statistical methods, machine learning approaches, and biological knowledge to support discovery in biomedical research.

The Research Officer will be expected to:

• Perform analysis of bulk RNA-seq and/or single-cell RNA-seq data, including quality control, normalization, clustering, differential expression analysis, and downstream biological interpretation
• Apply computational and statistical methods to identify patterns, cell states, perturbation responses, and biological signals from high-dimensional datasets
• Contribute to the development, testing, and maintenance of reproducible bioinformatics workflows and analysis pipelines
• Integrate machine learning or AI-based methods to enhance data analysis, interpretation, and hypothesis generation where applicable
• Work closely with experimental and computational team members to translate biological questions into appropriate analytical strategies
• Assist in the evaluation and validation of computational results using real-world biological datasets
• Document analysis steps, results, and interpretations clearly to support reproducibility and team communication
• Contribute to the broader development of ATOMIC as an AI-enabled platform for scalable and reproducible biological discovery

Prior exposure to bioinformatics pipelines, transcriptomics analysis, single-cell analysis, or machine learning methods is an advantage but not required. Candidates with strong computational foundations and a willingness to learn biological applications are encouraged to apply.

Job Requirements

The applicant should have:

• Bachelor’s or Master’s degree in Bioinformatics, Computational Biology, Computer Science, Data Science, Engineering, Statistics, Molecular Biology, or a related field
• Strong programming skills in Python and/or R
• Basic understanding of molecular biology, genomics, transcriptomics, or cell biology
• Familiarity with RNA-seq, single-cell RNA-seq, or related bioinformatics workflows is preferred
• Understanding of statistical methods and/or machine learning concepts is an advantage
• Ability to work with large datasets and maintain organized, reproducible analysis workflows
• Good written and verbal communication skills, with the ability to clearly document analysis results and explain findings to both computational and biological team members
• Curiosity, attention to detail, and interest in applying computational methods to biomedical discovery

This position provides an opportunity to work at the interface of genomics, AI, automation, and translational biology, and to contribute to the development of a platform designed to make biological discovery more scalable, reproducible, and actionable.