- Make original research contributions to enable machine learning model development, applied to cell and/or tissue biology, that impacts one or more critical problems in drug development.
- Identify and create novel ML approaches, model architecture, and training strategies, along with the required data to train.
- Analyse and tune experimental results to inform future experimental directions.
- Work within cross-functional ML Research, Chemistry, Engineering and Biology Teams, to direct research hypotheses and deliver outstanding research.
- Use your experience to undertake analysis of diverse computational biology datasets, including genetics, genomics, single-cell and bulk transcriptomics, proteomics, functional perturbation screens, imaging, knowledge graphs, PPI, clinical or other data types.
- Work in partnership with other Research & Development teams to evaluate the utility of research models, and incorporate feedback to ensure research outputs deliver high impact for drug design and development.
- Work with Bioinformatics, Data, and other groups to influence Iso’s datasets and pipelines strategy, to ensure innovative insights from these data are consistently brought to bear within drug development programmes.
- Perform thorough data analysis and data quality assurance checks, with a strong focus on accuracy and reproducibility, inline with industry standard processes.
- Work with other members of the Computational Biology team to deliver a unified team strategy.
- Report and present research findings and developments clearly and efficiently, and provide documentation, guidance, and communication on computational biology to the wider organisation.
- Experience in computational biology specializing in cell and/or tissue biology, with PhD and research experience (i.e. postdoctoral or industry experience), or equivalent experience
- Track record of delivery of outstanding research using deep learning techniques, including designing new ML architectures, hands-on experimentation, analysis, and visualisation
- Strong knowledge of linear algebra, calculus, probability, and statistics
- Demonstrated ability to write clean, idiomatic, and highly performant Python code
- Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas
- Expertise with detailed data quality control procedures and data visualisation
- Experience with experimental design and statistical analysis
- Demonstrated understanding of computational biology tools and methodologies and experience with the analysis of large -omics datasets
- Familiarity with a variety of assaying techniques, including NGS, cell-based assays, functional genomics, single-cell techniques, and image-based assays and their respective data analysis approaches
- Demonstrated understanding of the principles of molecular cell biology and genetics, or related biological disciplines
- Familiarity with data processing pipelines and tools
- Ability to effectively communicate scientific concepts to a variety of audiences
- Experience in using Git for version control and familiarity with CI/CD concepts
- Experience working in a Linux environment
- Demonstrated ongoing career progression / trajectory and a passion for learning