Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Smart Biomanufacturing with AI & Automation bridges biotechnology and artificial intelligence to revolutionize product yield, process efficiency, and quality control. This immersive course explores how AI and automation are transforming modern bioreactor operations through real-time monitoring, automated control, and adaptive responses. Across 4 Weeks, you will build practical fluency in real-time monitoring and automated control, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Smart Biomanufacturing with AI & Automation bridges biotechnology and artificial intelligence to revolutionize product yield, process efficiency, and quality control. This immersive course explores how AI and automation are transforming modern bioreactor operations through real-time monitoring, automated control, and adaptive responses.
📋 Course Objectives
1. Develop hands-on skill in real-time monitoring.
2. Master the fundamentals of automated control.
3. Translate biotechnology theory into practical, reproducible analysis.
4. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into real-time monitoring
🚀 Key Learning Outcomes
• Confidence to reason about real-time monitoring in real projects.
• Confidence to apply automated control in real projects.
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Smart Biomanufacturing
Introduce smart biomanufacturing principles and applications of AI. • Explore the intersection of biotechnology, AI, and automation. • Identify key challenges and opportunities in modern bioproduction.
Real-Time Bioreactor Monitoring & Control
Examine advanced sensor technologies for bioreactors. • Understand data acquisition, processing, and feedback loop mechanisms. • Implement real-time monitoring solutions for process optimization.
AI Algorithms for Adaptive Bioprocess Control
Introduce core AI concepts relevant to bioprocessing. • Apply AI algorithms for adaptive control in bioreactor operations. • Simulate adaptive control systems using tools like MATLAB/Simulink.
Machine Learning for Predictive Biomanufacturing
Discover various supervised and unsupervised ML techniques for bioprocesses. • Develop predictive models for yield, quality, and failure prevention. • Utilize ML for enhanced process optimization and anomaly detection.
Implementing Predictive Maintenance with ML
Learn to identify and prevent equipment failures proactively. • Conduct hands-on exercises using Python or TensorFlow for fault prediction. • Design robust predictive maintenance strategies for bioprocess equipment.
AI-Driven Analytics and Process Traceability
Explore the creation of AI dashboards for comprehensive process visibility. • Analyze complex bioprocess data for improved decision-making. • Ensure data integrity and traceability for regulatory requirements.
GMP Compliance in AI-Integrated Biomanufacturing
Understand current Good Manufacturing Practices (GMP) and their alignment with AI. • Address regulatory considerations for AI and automation in bioproduction. • Design AI systems that meet stringent quality and compliance standards.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Azure ML |
| Covered Tool / Platform | MATLAB |
| Covered Tool / Platform | Simulink |
| Covered Tool / Platform | Python |
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