Syngene IBAB Certificate Program in Artificial Intelligence in Life Sciences (SAILS)
Program Highlights
This 16-week Certificate Program in AILS is designed to equip graduates and postgraduates in Computer Science, Information Technology, Pharmacy, and Life Sciences disciplines such as Biotechnology, Microbiology, and Biochemistry with cutting-edge knowledge. The program provides a strong foundation in applying AI to solve complex problems in the Life Sciences industry, with an emphasis on real-life problem-solving skills.
Career Opportunities
The rapid and widespread adoption of machine learning, artificial intelligence, and data science across diverse disciplines has led to an exponentially growing demand for well-trained professionals who can implement these latest technologies to drive innovations in the space/domain of Life Science research and development. This has created demand for AI-literate life science professionals for future-ready career paths across industry, research, and healthcare.
Customised Curriculum
The course will provide foundational knowledge in applying AI to solve complex Life Science industry problems focusing on real-life case studies. The program is designed to nurture a new generation of biologists and IT engineers with complementary expertise in computation and biotechnology, essential for advancing AI applications.
Key Differentiators
- Gain a deeper understanding of relevant biological and mathematical concepts required for quantitative biological big data analysis.
- Hands-on experience in Python and R programming, with the ability to develop new machine learning and deep learning models.
- Knowledge and application of state-of-the-art AI tools commonly used to solve industry use cases related to biological data analysis.
- Exposure to widely used AI tools for productivity enhancement at work.
- Skills to develop and apply AI solutions for addressing business use cases in the Life Science industry.
Course Modules
M1. Foundations of Biological Sciences
- Central dogma and flow of genetic information
- Genetic engineering, Genome editing & Synthetic biology: Tools and technologies
- Analytical methods for DNA, RNA and protein
- Stem cells, Cell therapy, Biologics
- Omics, protein engineering, structural bioinformatics
- Examples of application of AI in addressing biological problems
M2. Foundations of Mathematics 1 - Calculus
- Derivatives, partial derivatives, higher-order derivatives, Taylor series
- Reimann integral, Average value of functions, differentials and error analysis
- Jacobian and Hessian matrices
M3. Foundations of Mathematics 2 - Probability and Statistics
- Probability theory, counting methods, conditional probability, bayes theorem
- Random variables, discrete and continuous probability distributions
- Central limit theorem and statistical tests
- Multivariate analysis, such as ANOVA
- Simulation techniques
M4. Foundations of Mathematics 3 - Linear Algebra
Vectors and Matrices
Column spaces, Row spaces, Null spaces, Rank
Projection Matrices
Eigenvalues and Eigenvectors
Quadratic forms, positive (semi)definite matrices
M5. Programming 1 - R
- Introduction to programming principles
- Data structures and algorithms
- Datatypes and variables
- Functions, conditions, loops
- Statistical analysis and simulation
- Visualization
- Handling files and libraries
M6. Programming 2 - Python
- Datatypes and variables in Python
- Functions, conditions, loops in Python
- Comprehensions, Lambda functions, Generator and Decorator functions
- Object oriented programming, classes and objects
- Modules, Packages, Exceptions
- File handling
- Numpy, Matplotlib
- Pandas
M7. Machine Learning
- Introduction
- Learning theory
- Supervised and Unsupervised Algorithms
- Kernel methods
- Practical design considerations
- Explainable AI
M8. Deep Learning
- Introduction
- Feedforward Neural Networks
- Training considerations
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Attention and Transformer Networks
M9. AI/ML tools in Computational Biology
- AlphaFold – Protein structure prediction
- AlphaGenome – DNA sequence model
- ESM3 – Protein Language Model
- EvoDiff – Gen AI for biomolecule discovery
- Pas-X Savvy or similar, GenAI based tools for Root Cause Analysis, Regulatory Dossier Generation, Clinical Trial Design, Pharmacovigilance
- Basic productivity tools like Microsoft Copilot
M10. Application of AI/ML in Biotech - Case Studies
- Case study 1 – Yield Optimization
- Case study 2 – Predictive Maintenance of equipment
- Case study 3 – De novo Molecular Design
- Case study 4 – Digital Twin of Bioreactors and Downstream processes
- Case study 5 – Digital Cell
M11. Soft Skills Intervention
- Presentation skills
- Critical Thinking and Problem Solving
- Personal branding
- Conflict Resolution
- Team work
- Time management
- E-Etiquette and Resume
- Negotiation skills
- Stakeholder Management
- Interview skills
Training Venue
Institute of Bioinformatics and Applied Biotechnology (IBAB) Electronics City Phase 1
Biocon Academy, Electronics City Phase 2, Bengaluru
Eligibility Criteria
Education Qualification:
- M Sc Life Sciences / Biotechnology / Microbiology / Biochemistry / other related disciplines in Life Sciences
- B Pharm / M Pharm
- BTech / MTech Biotechnology
- BE / BTech / ME / MTech / MCA / MSc – Computer Science, Information Technology or equivalent
Minimum CGPA/Percentage Required:
- CGPA: 6.5 or above on a scale of 10
- Percentage: 65%
Adherence to the eligibility criteria is mandatory. Kindly note the same and apply only if you fit the desired eligibility criteria as mentioned above.
Program Fee & Scholarship
The Biocon Merit Scholarship of 60%-75% of the program cost offered to all selected students will enable them to pursue this one-of-its-kind program at an affordable cost.
Student share: INR 2,00,000 + 18% GST
