Programme Parameters
Doctoral / R&D Specialist
8 Weeks (48 Hours) (48 Hrs)
Online
📅 Important Schedule Dates (Tentative)
01 Aug 2026 - 10:00 AM IST
26 Sep 2026 - 12:00 PM IST
28 Jul 2026 - 11:59 PM IST
About This Course
Set in deep science and technology, Industrial Deep Learning & Bio-Sequence Modeling is pitched at a doctoral and R&D level. To bridge advanced deep learning architectures with life science datasets, empowering researchers to build generative models for molecular biology. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
To bridge advanced deep learning architectures with life science datasets, empowering researchers to build generative models for molecular biology.
📋 Course Objectives
1. Put deep science and technology techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• PhD scholars and postdoctoral researchers working in deep science and technology
• R&D engineers and working professionals applying deep science and technology in industry
• Academics and educators building research or teaching capacity in deep science and technology
🚀 Key Learning Outcomes
• Tangible, reproducible deep science and technology work to show supervisors or employers.
• 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
Deep Learning Foundations for Life Sciences
• Multi-Layer Perceptrons & CNNs on sequence data
• Recurrent networks & sequence encoding pipelines
• Data pipelines for biological data formats (FASTA, PDB)
Transformers & Sequence Modeling
• Self-attention mechanisms and transformer blocks
• ESM-2 and biological protein language models
• Fine-tuning transformers for genomic sequence categorization
Geometric Deep Learning & Graph Neural Networks
• Graph representations of molecules and ligands
• Message passing algorithms, GCNs, and GATs
• Equivariant networks for molecular docking and protein folding
Generative Models & De Novo Design
• Generative diffusion models for molecule generation
• AlphaFold architecture overview and structural prediction
• Practical de novo protein design using PyTorch and open source checkpoints
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