Project Overview

In biopharmaceutical manufacturing, ensuring equipment is free from microbial contamination after cleaning is critical for product safety. Cleaning validation typically relies on compendial methods such as USP <61>, which assess microbial levels through culture-based techniques. However, these methods require multi-day incubation periods, specialized growth enrichment media, and manual operations, resulting in long turnaround times and increased risk of contamination during handling. The emergence of Rapid Microbial Methods (RMM) as state-of-art technologies seek to address these limitations posed by traditional compendial methods. Despite RMM having a shorter incubation period, the dependency of RMM on cells, high operator complexity and challenges in integrating process analytical technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation workflows.

This project is hosted within SMART-CAMP (Critical Analytics for Manufacturing Personalized-Medicine), an interdisciplinary research programme in Singapore (CREATE international research campus and innovation hub) and at the Massachusetts Institute of Technology (MIT). SMART CAMP addresses key technology bottlenecks in cell therapy manufacturing: (i) critical quality attributes of safe, effective cell therapy products; and (ii) integrated process analytics to monitor and modulate those attributes. This high-impact focus includes measurement and feedback control of processing parameters (process analytic technologies, or PAT) that contribute to cell viability and function during cell proliferation, and the measurement at intermediate and final steps of the cell product properties correlated with positive therapeutic outcomes (critical quality attributes, or CQA). This interdisciplinary team comprises engineers, biologists, clinicians, manufacturing, and data analytics experts from multiple MIT academic units, and multiple Singapore-based universities, research centres of excellence, and hospitals who are experienced at translational demonstrations of technologies in safety-regulated industries such as cell therapies.

Responsibilities

SMART-CAMP invites applications for a Postdoctoral Associate position in the area of microbiology and biotechnology, incorporating rapid detection tools and advancing them for testing in biopharmaceutical production. The successful candidate will be expected to work on the identification, verification, and design of ultraviolet spectroscopy and machine learning aided rapid and sensitive detection of micro-organisms, using a combination of microbiology, mass spectrometry and spectroscopic methods. This work will be conducted in close collaboration with a consortium of biopharmaceutical companies.

• This PDA will conduct feasibility studies of detection analytical methods based on metabolite detection of microorganisms to develop a rapid, sensitive diagnostic tool for safety screening.
• The PDA will take responsibility for testing and translating rapid methods for detection, as well as performing comparability/benchmarking experiments with compendial microbiology tests and molecular tests.
• Supervisory experience in junior researchers in all aspects of research.

The PDA will be expected to interface with technical staff from biopharma, as well as a research engineer to achieve the project objectives.

Requirements

• Ph.D. in microbiology, biotechnology, or relevant discipline.
• Demonstrable expertise in working with bacteria and/or fungi is desirable.
• An understanding of metabolite identification and quantification by LC-MS/MS, spectroscopic techniques is desirable. Experience or collaborative engagements involving machine learning / modelling is also viewed positively.
• Excellent verbal and written English communication skills are required.
• Strong interpersonal and relationship-building skills
• Self-motivated, independent, with superior collaborative, organizational and analytical skills
• Good track record of publication, IP and scientific output.

Interested applicants are invited to send in their full CV/resume, cover letter and list of three references (to include reference names and contact information). We regret that only shortlisted candidates will be notified.