Scientist, Analytical Science & Technology (Bioprocess Data Integration) BTI

The Bioprocessing Technology Institute (BTI), A.STAR, is seeking a highly motivated PhD-level computer scientist or AI systems researcher to support the development of multi-agent AI frameworks for biomanufacturing.

This role focuses on the design, integration, and orchestration of AI agents that interact with heterogeneous scientific models (e.g. mechanistic bioprocess models, data-driven predictors) to enable intelligent decision support for bioprocess development and manufacturing. You will work closely with biologists, bioinformaticians, and process modellers in a multidisciplinary research environment.

Key Responsibilities

• Design and implement multi-agent AI architectures for bioprocess modelling and optimisation
• Integrate diverse computational models (mechanistic, statistical, ML-based) into a coherent decision framework
• Develop agent communication, coordination, and decision arbitration strategies
• Build scalable and maintainable Python-based scientific software systems
• Collaborate with domain scientists to translate bioprocess constraints into computable logic
• Support validation of AI-assisted recommendations using experimental and historical process data
• Document system design, workflows, and assumptions for reproducibility and knowledge transfer

Qualifications

• PhD in Computer Science, Artificial Intelligence, Software Engineering, Applied Mathematics, or a closely related field
• Strong programming skills
• Solid foundation in software architecture, systems design, or distributed / agent-based systems
• Experience integrating multiple models, services, or workflows into a unified system
• Ability to work effectively in interdisciplinary scientific teams

Preferred/Advantageous Skills

• Experience with multi-agent systems, agent-based modelling, or workflow orchestration
• Familiarity with machine learning pipelines and model lifecycle management
• Exposure to scientific computing, simulation, optimisation, or digital twin concepts
• Experience working with noisy, incomplete, or experimental data
• Interest in biomanufacturing, biotechnology, or industrial bioprocesses (prior domain experience is not mandatory)