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Job Description

We are seeking two (2) talented postdoctoral researchers to join the Synthetic Biology Translational Research Programme (TRP) at the National University of Singapore’s Yong Loo Lin School of Medicine. Our programme applies synthetic biology to achieve positive clinical impact, with a focus on engineering microbial-based live biotherapeutics that can sense, compute, and deliver therapeutic functions in relevant biological environments.

The Research Fellows (RFs) will work closely with the Principal Investigator and the research team to develop microbial-based live biotherapeutics - from genetic circuit design and strain engineering through to functional testing and iteration. The RFs will be expected to drive projects, generate high-quality datasets, and contribute to manuscripts and grant deliverables. Appointments will be initially made on a 1-year contract, with the possibility of extension.

Main Responsibilities:
1. Design and build synthetic genetic circuits for live microbial therapeutics (e.g., sensing, logic, control of therapeutic payload expression) and engineer strains for robust function and stability.
2. Develop and apply high-throughput strain engineering and screening workflows to optimize therapeutic constructs and characterize strain libraries (e.g., potency, dynamic range, stability, metabolic burden), including implementation of genetic safeguards where appropriate.
3. Establish assays to evaluate live biotherapeutic function in relevant environments (e.g., simulated gut conditions, co-culture systems, anaerobic settings) and conduct in-vivo studies to evaluate therapeutic efficacy and safety in relevant biological models.
4. Mentor research assistants, graduate and undergraduate students. 
5. Assist in any other duties delegated by the Principal Investigator and group leaders. 

Remuneration will be commensurate to the candidate's qualifications and experience. Informal enquiries are welcome and should be made to Ms Suhana Sulaiman, at [email protected].

Formal application: Please submit your application, indicating job reference number and current/expected salary, supported by a detailed CV (including personal particulars, academic and employment history, complete list of publications, oral presentations, and full contacts of 3 referees). Only shortlisted candidates will be notified.

Qualifications

The ideal candidate will demonstrate deep, directly relevant experience and a publication record that shows significant scientific contributions.
 
Minimum Qualifications:
1. PhD in Biotechnology, Biological Sciences, Biochemistry, Bioengineering, Synthetic Biology, or a related discipline.
2. Strong hands-on experience in microbial synthetic biology, including genetic circuit design, strain engineering, and quantitative characterization relevant to live biotherapeutics (e.g., performance in complex media, stability, burden/fitness).
3. Proficiency in designing, executing, and iterating experiments (design–build–test–learn), including development of assays that reflect relevant biological environments and reproducible data analysis.
4. Evidence of driving projects with scientific rigor - from experimental planning to troubleshooting to clear communication - while collaborating effectively and mentoring junior team members.
5. Publication record demonstrating research ownership and impact (e.g., first-author paper(s) and/or co-first-author paper(s) in well-regarded international journals)

Highly Desirable Experience: 
1. Experience with high-throughput genetic engineering and screening workflows (e.g., multiplex genome editing, barcoded libraries, automation/liquid handling, flow cytometry/FACS-based screens, or similar).
2. Prior experience with microbial-based live biotherapeutics (e.g., probiotic/commensal chassis, anaerobic cultivation, genetic containment/kill switches, host–microbe interaction assays, or in vivo/ex vivo evaluation).
3. Proficiency in omics data analysis -including genomics, transcriptomics, proteomics, or metabolomics - to interpret complex biological datasets and derive actionable insights.
4. Experience applying artificial intelligence (AI) or machine learning (ML) approaches to protein or genetic circuit engineering.