Research Fellow (Metabolic Modelling for Biotechnology Chassis Optimisation (BII

Job description

A postdoctoral research fellow position (junior postdoc / senior postdoc) is available in the Breitling group in the Bioinformatics Institute (BII), A*STAR, Singapore in the area of Artificial Intelligence (AI) and Machine Learning (ML) for
Metabolic modelling for biotechnology chassis optimisation .


In the Breitling group, we believe that computational approaches can make a real difference for scientific progress in the life sciences.

We are committed to academic excellence and have created tools, such as antiSMASH, ipaPy2, RankProd and Selenzyme, that are used by thousands of researchers in academia and industry world-wide.

We value curiosity, creativity and innovation, and support the intellectual growth and career development of our team members.

Alumni of the Breitling group have moved on to build their own start-up companies and have joined the faculty of major universities.


The successful candidate will be part of an interdisciplinary team working on the development of computational tools to support the Design–Build–Test–Learn cycle of synthetic biology, in close collaboration with the team of experimental scientists at A*STAR’s newly established Singapore Integrative Biosystems & Engineering Research Strategic Research and Translational Thrust (SIBER SRTT).


Job Descriptions:
Develop and validate novel AI and ML models and methods for Metabolic modelling for biotechnology chassis optimisation.


Apply these and other state-of-the-art computational tools to inform, guide and support the work of experimental biologists and biosystems engineers.


Work closely with the other members of the computational synthetic biology team in BII, as well as with the team of experimental biologists at SIBER SRTT, to realise the shared mission of driving synthetic biology advances towards industrial implementation.


Present findings at seminars, meetings and national / international conferences.


Write manuscripts for publication in high-impact scientific journals.


Prepare high-quality software for use by the wider community of researchers.


Apply proactively for individual research grants as part of career development and contribute to the writing of larger grant applications.


Build and maintain strong relationships with the local and international research network.


Requirements:
PhD in Computer Science, Bioinformatics, Computational Biology, Biochemistry, Biotechnology, Chemical Engineering, Microbiology or a related field.


Excellent coding and software development skills, including proficiency in Python and R programming.


Familiarity with state-of-the-art computational tools, methodology and data formats relevant for computational biology and bioengineering, with a special focus on molecular biology and metabolic engineering.


Passion for joining a growing and dynamic group of researchers and a demonstrated ability to work independently as well as collaboratively and efficiently in an interdisciplinary team.


Desire to work closely with experimental biologists to develop tools to drive the new green revolution towards decarbonisation and sustainable chemical production.


Track record of peer-reviewed scientific publications in internationally recognised journals.


Excellent communication skills both written and spoken, especially for the preparation of scientific manuscripts and reports.


A strong interest in the translational and applied aspects of research, including collaborations with industrial partners.


Preferred Skills:
Whether you are an experienced bioinformatician excited about the possibilities of artificial intelligence, or a biology-curious machine learning expert looking for new exciting applications across disciplines, we want to hear from you.

A strong willingness to learn and develop new skills is highly preferred.

We will be looking for candidates with any of the following qualities:
Experience with the application of AI/ML tools for the automated parameterisation of genome-scale metabolic models and the generation of AI virtual cells.


Familiarity with the tools and concepts of constraint-based metabolic modelling, including resource-constrained models, metabolic network reconstruction and metabolic flux analysis.


Deep understanding of the methodology, aims and concepts of microbial strain engineering for biotechnology.


Strong background in AI/ML modelling, statistics, and data analysis and visualization, preferably including practical experience with deep learning frameworks (e.g., TensorFlow, PyTorch).


Extensive domain knowledge in (bio)chemistry, molecular biology, microbiology and the concepts of synthetic biology / engineering biology.


Familiarity with the techniques of agentic AI.


We offer:
A nurturing, supportive and stimulating environment with close mentorship to develop your scientific career and shape the future of synthetic biology through innovative computational approaches.


An opportunity to work closely with an ambitious team of experimental biologists at the forefront of advances in synthetic biology.


Access to state-of-the-art technologies, scientific resources, core facilities and infrastructure housed within A*STAR to facilitate your research work.


Access to collaborations with industrial partners to translate research findings to commercial applications and societal impact.


We welcome applicants from all background.


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