The Innovation & Enterprise Fellowship Programme (IFP) seeks to grow the pool of deep-tech talent in Singapore with the capabilities to support the commercialisation of deep-tech research and bring nascent technologies to market. Targeting researchers, scientists, and engineers (RSEs), the programme aims to develop industry relevant innovation and entrepreneurial skills through both formal and on-the-job training.
At EDDC, as Singapore’s national platform for drug discovery and development. We are committed to discover and develop novel therapeutics by working collaboratively with both public sector and industry partners to translate innovations from bench to clinic using industry relevant and cutting-edge technology platforms. This includes tackling complex and unmet medical challenges in disease areas such as oncology, fibrosis, infectious and autoimmune diseases. EDDC is now seeking under the IFP programme a highly skilled and motivated Bioinformatics Scientist/Engineer to join the Computational Biology group to drive data-driven discovery of therapeutic targets and clinically relevant biomarkers, achieving wholistic understanding of human disease biology and pathology.
This is an eighteen months on-the-job training programme that requires participation in and supporting cross-functional and collaborative drug discovery projects. By joining EDDC, the IFP candidate will be part of a dynamic, interdisciplinary team geared to enhance and facilitate the drug discovery journey through data harmonization and deconvolution, multimodal data analytics, and advanced machine learning. The role will also leverage strategic partnerships both locally and globally, to adopt a flexible approach in refining and advancing EDDC’s solutions.
Responsibilities:
1. Multimodal data analytics for target, biomarker discovery and MoA elucidation
o Develop and implement computational strategies for integrated analysis across diverse biological datasets, including but not limited to genetics/genomics, transcriptomics (bulk- and single-cell RNAseq) and proteomics data.o Employ state-of-the-art machine learning and AI techniques to uncover patterns in extensive biological datasets, developing predictive models that inform therapeutic strategies.o Collaborate with cross-functional colleagues to drive the data-driven discovery and prioritization of novel therapeutic targets and clinically relevant biomarkers, integrating advanced analytics to elucidate the molecular mechanisms of action.
2. Data Integration and automation
o Evaluate and harmonize multi-dimensional data to ensure data quality and consistency.
o Architect and manage databases for cross-functional biological data from public and proprietary sources.
o Optimise and automate workflows when applicable, streamline data access for researchers to facilitate high-throughput analysis and collaborative research efforts.
3. Collaborative Research and strategic partnerships
o Work closely with experimental biologists, chemists, and other computational biologists to design experiments, analyse results, and communicate findings in scientific reports and presentations.
o Establish and maintain partnerships with leading computational biology, data science, and AI entities both locally and globally, serving as the computational biology liaison to ensure effective communication and alignment of objectives between external partners and EDDC’s internal project teams.
4. Innovation and Continuous Learning
o Stay abreast of the latest developments in bioinformatics, data science, machine learning, and AI as they pertain to drug discovery and development.
o Implement new technologies and methodologies to refine in-house workflows and enhance EDDC’s computational platforms.
Requirements:
- PhD in Computational Biology, Bioinformatics, Computer Science, or a related field, with a strong emphasis on data analytics and machine learning in biological contexts. Minimally 2 years of postdoc experience or equivalent in industry settings.
- Applicants with suitable aptitude, potential and interest in harnessing big-data and advanced analytics for enhancing and accelerating drug discovery.
- Demonstrated experience in multi-omics data analysis (eg. genetics/genomics, bulk and single-cell transcriptomics, proteomics, etc.) and the application of machine learning in biological research.
- Proficiency in software engineering and programming languages such as Python and R, and familiarity with data engineering techniques including cloud-based and on-premises database services, database management systems, APIs and workflow development frameworks.
- Excellent communication and collaboration skills, with experience working independently and in multi-disciplinary teams.
- Keen to learn new techniques and adaptable to changing priorities.
- Proficiency in machine learning and AI methodologies for analysing large-scale biological data, with a proven track record of developing predictive models to guide therapeutic development and strategy is an advantage.
- Industry experience in pharmaceutical or biotech companies is an advantage.
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.
