AI/ML for CRISPR Genome Engineering Hands-On Training Program - From gRNA Design to LLM Agents - With LIVE Project Work

High-Growth Careers Need Hybrid Skills

Biology + AI + Genomics + CRISPR is becoming one of the most powerful skill combinations for modern biotech careers.

AI/ML for CRISPR Genome Engineering Hands-On Training Program 

From gRNA Design to LLM Agents 

 With LIVE Project Work & Placement Assistance

Starts 3rd June 2026

The next generation of genome engineers will not just design guide RNAs, they will train models that design them better, predict off-targets faster, and turn weeks of wet-lab trial-and-error into hours of in silico precision.

This 45-day intensive program, followed by project work is built for life sciences students and early-career professionals who want to enter the highest-paying frontier of modern biotech: AI-driven genome editing.

No prior coding experience needed. By the end, you will have built your own end-to-end CRISPR design pipeline using Python, deep learning, and the same tools that companies like Profluent, Metagenomi, and insitro are using right now.

COURSE SUMMARY AT A GLANCE

WHY LEARN AI IN CRISPR? 

CRISPR was supposed to make gene editing simple. In practice, it created a new problem: choosing the right guide RNA out of thousands of possibilities, predicting whether it will cut the wrong place, and designing therapies safe enough for human trials.

Traditional rule-based design tools (like Rule Set 3) get you only so far. They miss context, ignore chromatin state, and cannot scale to genome-wide screens. This is exactly where AI/ML steps in.

Today:

• DeepCRISPR predicts on-target activity with accuracy traditional methods cannot match.
• DNABERT and transformer models read DNA the way GPT reads language.
• Profluent's OpenCRISPR-1 is the world's first AI-generated, openly licensed gene editor.
• LLM agents like CRISPR-GPT are automating gRNA selection, off-target analysis, and experiment planning across 22+ tasks.

The companies driving this revolution are hiring but they are not hiring biologists who only know wet-lab CRISPR. They are hiring people who can sit between biology and AI. People who understand both the Cas9 mechanism and the loss function of the model predicting where it cuts.

This program creates exactly that person.

WHY ENROL FOR THIS PROGRAM?

1. You learn both sides - CRISPR biology and machine learning, taught in a way that connects them at every step. Most courses teach one or the other.
2. You build, not just watch - every day has a 45–50 minute hands-on practical. By the end, you will have written code for one-hot encoding, GC content scoring, RF/SVM classifiers, CNNs, LSTMs, DNABERT fine-tuning, and a full automated gRNA design pipeline.
3. You get a real research project under PI mentorship - not a self-paced capstone, but a structured project with a named faculty mentor and a 3, 6, or 12-month timeline.
4. You work with the real tools used in industry - Biopython, scikit-learn, Keras, ViennaRNA, CRISPick, MAGeCK, DeepCRISPR, DNABERT, LangChain, and CRISPR-GPT-style LLM agents.
5. You learn the business and ethics too - FDA guidelines, OpenCRISPR-1 patents, regulatory landscape, and responsible AI in gene editing. This is what employers actually ask about in interviews.
6. You finish with a portfolio, not just a certificate - including your own gRNA design pipeline, model benchmarking reports, off-target predictions, a written policy brief on responsible AI, and a PI-mentored research project.
7. It is built for Indian life sciences students - fresher-friendly, conversational, and priced for accessibility, but with the depth that gets you taken seriously by global biotech companies.

WHO IS THIS PROGRAM FOR? (Eligibility)

This program is designed for:

• M.Sc. / B.Tech / M.Tech students in Biotechnology, Bioinformatics, Life Sciences, Genetics, Molecular Biology, Microbiology, Biochemistry, or Pharmacy
• Ph.D. scholars working in molecular biology, gene editing, or computational biology who want to add AI/ML to their toolkit
• Early-career researchers and lab professionals working with CRISPR who feel limited by manual gRNA design tools
• Final-year B.Sc. students with a strong interest in genome engineering and a willingness to learn programming
• Working professionals from biotech, pharma, or clinical research who want to transition into AI-driven biology roles
• Faculty members upgrading their teaching and research portfolios

Prerequisites:

• Basic understanding of molecular biology and DNA
• A laptop with internet access
• Willingness to learn Python, we teach from scratch starting Day 4
• No prior coding experience required
• No prior machine learning experience required

WHAT YOU WILL LEARN — KEY OUTCOMES

By the end of the program, you will be able to:

• Explain the molecular biology of CRISPR-Cas9, base editing, and prime editing in depth
• Write Python code to parse genomic data, encode DNA sequences, and compute biological features
• Build and train classical ML models (logistic regression, random forest, SVM) for gRNA efficiency prediction
• Build deep learning models (CNN, LSTM, transformer-based) for on-target and off-target prediction
• Fine-tune DNABERT and use transfer learning on biological sequence data
• Design base and prime editing strategies using AI tools
• Run pooled CRISPR screen analysis with MAGeCK
• Build LLM agents (LangChain) that automate gRNA design pipelines
• Analyse single-cell CRISPR screen data (Perturb-seq, CITE-seq)
• Evaluate models using AUC-ROC, MCC, F1, and SHAP for interpretability
• Write policy briefs on regulatory and ethical aspects of AI in gene editing
• Independently execute a research project under PI mentorship

Download Brochure To check complete details on Course curriculum, project list & scientist details

DOWNLOAD LINK

PROGRAM STRUCTURE — 7 WEEKS, 45 DAYS

Each session = 20–25 minutes of theory + 45–50 minutes of hands-on practical.

(A detailed day-wise breakdown is available in the brochure.)

HOW THE PROGRAM WORKS

This is a live, instructor-led program — not a pre-recorded video library. You learn in real time, ask questions in real time, and build alongside the faculty.

Phase 1 — Live Training (45 Days)

Daily 1-hour live sessions from 7:00 PM to 8:00 PM IST, starting 29th May 2026. Every session is split into structured theory followed by a hands-on practical, so you are not just watching — you are coding, designing, and analysing from Day 1.

All live sessions are recorded and available for lifetime access, so you never lose a class even if you miss it live.

Phase 2 — Project Work Under PI Mentorship

After completing the 45-day training, you move into the project phase — and this is where the program becomes genuinely career-defining.

You choose a research project from a curated list of real, faculty-designed projects across three duration tracks:

• 3-Month Projects — focused, single-objective projects ideal for building a strong portfolio piece. Examples include guide RNA efficiency prediction models, AI-based variant identification, CNN-based on-target predictors, and AI-driven sgRNA design for cancer driver gene knockout.
• 6-Month Projects — deeper, multi-stage projects suitable for dissertation-level work or serious portfolio development. Examples include off-target site prediction using ML, deep learning-based gRNA design, transformer-based off-target prediction for sickle cell therapy, multi-omics CRISPR efficiency modelling, and CRISPR-GPT agents for designing gene therapies.
• 12-Month Projects — research-grade, publication-ready projects for those targeting Ph.D. applications, research publications, or specialised industry roles. Examples include multi-task deep learning models for CRISPR design optimisation, AI-driven discovery of novel PAM-specific Cas variants, multi-omics AI platforms for personalised cancer therapy, generative AI for novel CRISPR enzymes, and CRISPR-ML benchmarking platforms.

Each project is assigned to a Principal Investigator (PI) — a faculty mentor who guides you through problem definition, methodology, execution, and final deliverables. You are not figuring things out alone; you are working under structured research supervision.

Project categories span across:

• gRNA design and on-target efficiency prediction
• Off-target prediction and explainable AI
• Deep learning architectures for CRISPR (CNN, LSTM, transformers)
• Base editing and prime editing outcome prediction
• Multi-omics integration (epigenomics, transcriptomics, chromatin accessibility)
• Generative AI for novel Cas enzymes and PAM specificity
• LLM agents and CRISPR-GPT-style automation
• Disease-specific CRISPR therapy design (sickle cell, cystic fibrosis, Duchenne muscular dystrophy, cancer)
• Benchmarking and comparative ML studies

Phase 3 — Certification & Portfolio

On successful completion of training and your chosen project, you receive your Biotecnika certification along with a documented project portfolio that you can showcase in interviews, Ph.D. applications, and on LinkedIn.

WHY THE PROJECT PHASE MATTERS

Most online courses end with a certificate. This one ends with proof of work.

Recruiters and Ph.D. admission committees increasingly want to see what you have actually built — not just what you have attended. The project phase is designed exactly for this:

• You work on real research questions, not toy datasets
• You are mentored by a named PI, which adds credibility
• Your final output is publication-ready or interview-ready
• You build a portfolio that differentiates you from candidates with generic ML certifications

This is the difference between "I took a course on CRISPR and AI" and "I built a transformer-based off-target predictor under PI mentorship over six months." Employers and admission committees notice.

TOOLS & TECHNOLOGIES YOU WILL WORK WITH

• Programming: Python, NumPy, Pandas, Matplotlib, Seaborn
• Biology-specific libraries: Biopython, ViennaRNA, scanpy
• Machine learning: scikit-learn, SHAP, XGBoost
• Deep learning: Keras, TensorFlow, PyTorch
• Transformer models: DNABERT
• CRISPR-specific tools: CRISPick, CRISPy, DeepCRISPR, CnnCrispr, CRISTA, BE-Designer, PrimeDesign, PAMmla, MAGeCK, MAGeCK-Flute
• LLM agents: LangChain, CRISPR-GPT-style workflows
• Data sources: NCBI Entrez, CRISPRbrain, CRISPick API
• Version control: Git basics

HANDS-ON PROJECTS YOU WILL BUILD DURING TRAINING

• A GC content and dinucleotide frequency dashboard (Week 1)
• A gRNA feature extractor processing 1000+ guides (Week 3)
• Benchmark report comparing logistic regression, random forest, and SVM
• A CNN-based on-target activity predictor (Week 4)
• Fine-tuned DNABERT model for off-target prediction (Week 5)
• An automated end-to-end pipeline: gene name → fetch sequence → design gRNAs → score → ranked CSV (Week 6)
• A LangChain LLM agent that calls CRISPick and NCBI APIs (Week 7)
• A 1-page policy brief on responsible AI in gene editing
• ML risk stratification model on simulated patient genotypes (Week 7)

These are in addition to your final PI-mentored research project.

CAREER OUTCOMES — WHERE THIS TAKES YOU

Roles this program prepares you for:

• Computational Biologist (CRISPR / Gene Editing focus)
• AI/ML Scientist in Biotech
• gRNA Design Specialist
• Bioinformatics Scientist (Genome Engineering)
• Research Associate in CRISPR therapeutics
• Cell Engineering Computational Scientist
• Genomic Data Scientist
• Computational Geneticist
• Faculty / Research Scholar in computational genome engineering

Industries hiring for these skills: gene therapy companies, CRISPR diagnostics, agricultural biotech, pharmaceutical R&D, contract research organisations (CROs), cell and gene therapy startups, academic research labs, and AI-biotech crossover companies.

WHY BIOTECNIKA?

Biotecnika has trained thousands of life sciences students and professionals across India. Our certifications are designed by working scientists and educators who understand both Indian academic realities and global industry expectations.

This program is part of our growing AI-in-Life-Sciences vertical, alongside our PG Diploma in AI/ML & Data Science, AI for Drug Designing workshops, and bioinformatics certifications.

CERTIFICATION

On successful completion of all 45 days of training and your assigned PI-mentored project, participants receive a Certificate in AI/ML for CRISPR Genome Engineering from Biotecnika Info Labs Pvt. Ltd. — verifiable, suitable for adding to LinkedIn, your CV, and Ph.D. or job applications. Additionally, Project students will get an Experience letter. People who register for 6 & 12 Months project will get a Letter of Recommendation.

FREQUENTLY ASKED QUESTIONS

Q. I am from a pure biology background. Can I really learn AI/ML in 45 days? Yes. The first two weeks are designed exactly for you we start from "what is a variable" and build up to neural networks. The pacing is fresher-friendly.

Q. I already know Python. Will this still be useful? Yes. Weeks 3 onwards assume Python knowledge and go deep into ML, DL, transformers, and LLM agents — content that is rarely available in a single Indian program.

Q. How does the project phase work? After the 45-day training, you select a project from our curated list across 3-month, 6-month, or 12-month tracks. Each project is assigned to a Principal Investigator who mentors you through the entire research process.

Q. Can I choose my project duration? Yes. You select the track that fits your timeline and goals — a 3-month project for portfolio-building, 6-month for dissertation-level depth, or 12-month for publication-ready research.

Q. Do I need a powerful computer? No. Most practicals run in Google Colab (free GPU access). A standard laptop with internet is sufficient.

Q. How is this different from a generic AI/ML course? Generic ML courses teach you to predict house prices and classify cats. This program teaches you to predict gRNA efficiency and detect off-target cuts — using real biological data, real tools, and real research papers, with a real research project at the end.

Q. What if I miss a live class? All sessions are recorded and available for lifetime access.

Q. Will the recordings be available afterwards? Yes, lifetime access to all recordings and course material.

Genome editing is no longer just a wet-lab discipline. The future belongs to scientists who can write the algorithms that design the experiments.

Starting 3rd June 2026, you can become one of them.

Limited seats. Closed-door cohort. Enrol now.