{"product_id":"ai-ml-for-crispr-genome-engineering-hands-on-training-program-from-grna-design-to-llm-agents-with-live-project-work","title":"AI\/ML for CRISPR Genome Engineering Hands-On Training Program - From gRNA Design to LLM Agents  - With LIVE Project Work","description":"\u003ch3 style=\"text-align: center;\" data-end=\"1977\" data-start=\"1919\"\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eHigh-Growth Careers Need Hybrid Skills\u003c\/span\u003e\u003c\/h3\u003e\n\u003ch3 style=\"text-align: center;\" data-end=\"1977\" data-start=\"1919\"\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eBiology + AI + Genomics + CRISPR is becoming one of the most powerful skill combinations for modern biotech careers.\u003c\/span\u003e\u003c\/h3\u003e\n\u003ch2 class=\"text-text-100 mt-3 -mb-1 text-[1.375rem] font-bold\" style=\"text-align: center;\"\u003eAI\/ML for CRISPR Genome Engineering Hands-On Training Program \u003c\/h2\u003e\n\u003ch2 class=\"text-text-100 mt-3 -mb-1 text-[1.375rem] font-bold\" style=\"text-align: center;\"\u003eFrom gRNA Design to LLM Agents \u003c\/h2\u003e\n\u003ch2 class=\"text-text-100 mt-3 -mb-1 text-[1.375rem] font-bold\" style=\"text-align: center;\"\u003e With LIVE Project Work \u0026amp; Placement Assistance\u003c\/h2\u003e\n\u003ch2 style=\"text-align: center;\"\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eStarts 3rd June 2026\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThe 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eNo 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eCOURSE SUMMARY AT A GLANCE\u003c\/strong\u003e\u003c\/p\u003e\n\u003cdiv class=\"overflow-x-auto w-full px-2 mb-6\"\u003e\n\u003ctable class=\"min-w-full border-collapse text-sm leading-[1.7] whitespace-normal\" style=\"width: 99.9644%; height: 313.6px;\"\u003e\n\u003cthead class=\"text-left\"\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003cth scope=\"col\" class=\"text-text-100 border-b-0.5 border-border-300\/60 py-2 pr-4 align-top font-bold\" style=\"width: 34.9319%; height: 19.6px;\"\u003eDetail\u003c\/th\u003e\n\u003cth scope=\"col\" class=\"text-text-100 border-b-0.5 border-border-300\/60 py-2 pr-4 align-top font-bold\" style=\"width: 64.636%; height: 19.6px;\"\u003eInformation\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eProgram Name\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003eAI\/ML for CRISPR Genome Engineering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eStart Date\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003e3rd June 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eDuration\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003e45 days of live training + 3,6,12 months project work\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eClass Timings\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003e7:00 PM – 8:00 PM IST\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eMode\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003eLive online sessions (with recorded access)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 39.2px;\"\u003eFormat\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 39.2px;\"\u003eDaily theory+ hands-on\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 39.2px;\"\u003eProject Work\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 39.2px;\"\u003e3-month \/ 6-month \/ 12-month options under PI mentorship\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 39.2px;\"\u003eFaculty\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 39.2px;\"\u003eIndustry experts and senior researchers in CRISPR and AI\/ML\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003eCertification\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003eCertificate of Completion from Biotecnika, Experience letter with Project work only\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 39.2px;\"\u003eEligibility\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 39.2px;\"\u003eLife sciences, biotech, pharma, and bioinformatics students and professionals\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 34.9319%; height: 19.6px;\"\u003ePrerequisites\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\" style=\"width: 64.636%; height: 19.6px;\"\u003eBasic biology; no prior coding required\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e \u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHY LEARN AI IN CRISPR? \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eCRISPR 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eTraditional 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eToday:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eDeepCRISPR\u003c\/strong\u003e predicts on-target activity with accuracy traditional methods cannot match.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eDNABERT\u003c\/strong\u003e and transformer models read DNA the way GPT reads language.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eProfluent's OpenCRISPR-1\u003c\/strong\u003e is the world's first AI-generated, openly licensed gene editor.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eLLM agents like CRISPR-GPT are automating gRNA selection, off-target analysis, and experiment planning across 22+ tasks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThe 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis program creates exactly that person.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHY ENROL FOR THIS PROGRAM?\u003c\/strong\u003e\u003c\/p\u003e\n\u003col class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou learn both sides\u003c\/strong\u003e - CRISPR biology and machine learning, taught in a way that connects them at every step. Most courses teach one or the other.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou build, not just watch\u003c\/strong\u003e - 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.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou get a real research project under PI mentorship\u003c\/strong\u003e - not a self-paced capstone, but a structured project with a named faculty mentor and a 3, 6, or 12-month timeline.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou work with the real tools used in industry\u003c\/strong\u003e - Biopython, scikit-learn, Keras, ViennaRNA, CRISPick, MAGeCK, DeepCRISPR, DNABERT, LangChain, and CRISPR-GPT-style LLM agents.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou learn the business and ethics too\u003c\/strong\u003e - FDA guidelines, OpenCRISPR-1 patents, regulatory landscape, and responsible AI in gene editing. This is what employers actually ask about in interviews.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eYou finish with a portfolio\u003c\/strong\u003e, 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.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eIt is built for Indian life sciences students\u003c\/strong\u003e - fresher-friendly, conversational, and priced for accessibility, but with the depth that gets you taken seriously by global biotech companies.\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHO IS THIS PROGRAM FOR? (Eligibility)\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis program is designed for:\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eM.Sc. \/ B.Tech \/ M.Tech students in Biotechnology, Bioinformatics, Life Sciences, Genetics, Molecular Biology, Microbiology, Biochemistry, or Pharmacy\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003ePh.D. scholars working in molecular biology, gene editing, or computational biology who want to add AI\/ML to their toolkit\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eEarly-career researchers and lab professionals working with CRISPR who feel limited by manual gRNA design tools\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eFinal-year B.Sc. students with a strong interest in genome engineering and a willingness to learn programming\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eWorking professionals from biotech, pharma, or clinical research who want to transition into AI-driven biology roles\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eFaculty members upgrading their teaching and research portfolios\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003ePrerequisites:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBasic understanding of molecular biology and DNA\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA laptop with internet access\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eWillingness to learn Python, we teach from scratch starting Day 4\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eNo prior coding experience required\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eNo prior machine learning experience required\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHAT YOU WILL LEARN — KEY OUTCOMES\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eBy the end of the program, you will be able to:\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eExplain the molecular biology of CRISPR-Cas9, base editing, and prime editing in depth\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eWrite Python code to parse genomic data, encode DNA sequences, and compute biological features\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBuild and train classical ML models (logistic regression, random forest, SVM) for gRNA efficiency prediction\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBuild deep learning models (CNN, LSTM, transformer-based) for on-target and off-target prediction\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eFine-tune DNABERT and use transfer learning on biological sequence data\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eDesign base and prime editing strategies using AI tools\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eRun pooled CRISPR screen analysis with MAGeCK\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBuild LLM agents (LangChain) that automate gRNA design pipelines\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eAnalyse single-cell CRISPR screen data (Perturb-seq, CITE-seq)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eEvaluate models using AUC-ROC, MCC, F1, and SHAP for interpretability\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eWrite policy briefs on regulatory and ethical aspects of AI in gene editing\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eIndependently execute a research project under PI mentorship\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDownload Brochure To check complete details on Course curriculum, project list \u0026amp; scientist details\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003ca href=\"https:\/\/www.biotecnika.org\/wp-content\/uploads\/2026\/05\/AI_ML_CRISPR_Brochure-1.pdf\"\u003e\u003cstrong\u003eDOWNLOAD LINK\u003c\/strong\u003e\u003c\/a\u003e\u003c\/p\u003e\n\u003cdiv style=\"position: relative; padding-top: max(60%,324px); width: 100%; height: 0;\"\u003e\u003ciframe style=\"position: absolute; border: none; width: 100%; height: 100%; left: 0; top: 0;\" src=\"https:\/\/online.fliphtml5.com\/wvmgz\/AI_ML_CRISPR_Brochure\/\" title=\"AI_ML_CRISPR_Brochure\"\u003e\u003c\/iframe\u003e\u003c\/div\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003ePROGRAM STRUCTURE — 7 WEEKS, 45 DAYS\u003c\/strong\u003e\u003c\/p\u003e\n\u003cdiv class=\"overflow-x-auto w-full px-2 mb-6\"\u003e\n\u003ctable class=\"min-w-full border-collapse text-sm leading-[1.7] whitespace-normal\"\u003e\n\u003cthead class=\"text-left\"\u003e\n\u003ctr\u003e\n\u003cth scope=\"col\" class=\"text-text-100 border-b-0.5 border-border-300\/60 py-2 pr-4 align-top font-bold\"\u003eWeek\u003c\/th\u003e\n\u003cth scope=\"col\" class=\"text-text-100 border-b-0.5 border-border-300\/60 py-2 pr-4 align-top font-bold\"\u003eTheme\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 1 (Days 1–7)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eIntroduction to CRISPR Biology \u0026amp; The Need for AI\/ML\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 2 (Days 8–14)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003ePython \u0026amp; Data Science Bootcamp for Biotech\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 3 (Days 15–21)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eSequence Representation \u0026amp; Core ML for On-Target Prediction\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 4 (Days 22–28)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eDeep Learning for gRNA Design\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 5 (Days 29–35)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eOff-Target Prediction, Transformers \u0026amp; Advanced Gene Editing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 6 (Days 36–42)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eIndustry Integration, Automation \u0026amp; Ethics\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eWeek 7 (Days 43–45)\u003c\/td\u003e\n\u003ctd class=\"border-b-0.5 border-border-300\/30 py-2 pr-4 align-top\"\u003eAdvanced Topics \u0026amp; Global Trends\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eEach session = 20–25 minutes of theory + 45–50 minutes of hands-on practical.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e(\u003cem\u003eA detailed day-wise breakdown is available in the brochure.\u003c\/em\u003e)\u003c\/p\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eHOW THE PROGRAM WORKS\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003ePhase 1 — Live Training (45 Days)\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eDaily 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eAll live sessions are recorded and available for lifetime access, so you never lose a class even if you miss it live.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003ePhase 2 — Project Work Under PI Mentorship\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eAfter completing the 45-day training, you move into the project phase — and this is where the program becomes genuinely career-defining.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eYou choose a research project from a curated list of real, faculty-designed projects across three duration tracks:\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003e3-Month Projects\u003c\/strong\u003e — 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.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003e6-Month Projects\u003c\/strong\u003e — 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.\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003e12-Month Projects\u003c\/strong\u003e — 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.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eEach 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eProject categories span across:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003egRNA design and on-target efficiency prediction\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eOff-target prediction and explainable AI\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eDeep learning architectures for CRISPR (CNN, LSTM, transformers)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBase editing and prime editing outcome prediction\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eMulti-omics integration (epigenomics, transcriptomics, chromatin accessibility)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eGenerative AI for novel Cas enzymes and PAM specificity\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eLLM agents and CRISPR-GPT-style automation\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eDisease-specific CRISPR therapy design (sickle cell, cystic fibrosis, Duchenne muscular dystrophy, cancer)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBenchmarking and comparative ML studies\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003ePhase 3 — Certification \u0026amp; Portfolio\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eOn 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.\u003c\/p\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHY THE PROJECT PHASE MATTERS\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eMost online courses end with a certificate. This one ends with proof of work.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eRecruiters 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:\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eYou work on real research questions, not toy datasets\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eYou are mentored by a named PI, which adds credibility\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eYour final output is publication-ready or interview-ready\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eYou build a portfolio that differentiates you from candidates with generic ML certifications\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eTOOLS \u0026amp; TECHNOLOGIES YOU WILL WORK WITH\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eProgramming:\u003c\/strong\u003e Python, NumPy, Pandas, Matplotlib, Seaborn\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eBiology-specific libraries:\u003c\/strong\u003e Biopython, ViennaRNA, scanpy\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eMachine learning:\u003c\/strong\u003e scikit-learn, SHAP, XGBoost\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eDeep learning:\u003c\/strong\u003e Keras, TensorFlow, PyTorch\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eTransformer models:\u003c\/strong\u003e DNABERT\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eCRISPR-specific tools:\u003c\/strong\u003e CRISPick, CRISPy, DeepCRISPR, CnnCrispr, CRISTA, BE-Designer, PrimeDesign, PAMmla, MAGeCK, MAGeCK-Flute\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eLLM agents:\u003c\/strong\u003e LangChain, CRISPR-GPT-style workflows\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eData sources:\u003c\/strong\u003e NCBI Entrez, CRISPRbrain, CRISPick API\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003e\n\u003cstrong\u003eVersion control:\u003c\/strong\u003e Git basics\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eHANDS-ON PROJECTS YOU WILL BUILD DURING TRAINING\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA GC content and dinucleotide frequency dashboard (Week 1)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA gRNA feature extractor processing 1000+ guides (Week 3)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBenchmark report comparing logistic regression, random forest, and SVM\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA CNN-based on-target activity predictor (Week 4)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eFine-tuned DNABERT model for off-target prediction (Week 5)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eAn automated end-to-end pipeline: gene name → fetch sequence → design gRNAs → score → ranked CSV (Week 6)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA LangChain LLM agent that calls CRISPick and NCBI APIs (Week 7)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eA 1-page policy brief on responsible AI in gene editing\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eML risk stratification model on simulated patient genotypes (Week 7)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThese are in addition to your final PI-mentored research project.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eCAREER OUTCOMES — WHERE THIS TAKES YOU\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eRoles this program prepares you for:\u003c\/p\u003e\n\u003cul class=\"[li_\u0026amp;]:mb-0 [li_\u0026amp;]:mt-1 [li_\u0026amp;]:gap-1 [\u0026amp;:not(:last-child)_ul]:pb-1 [\u0026amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eComputational Biologist (CRISPR \/ Gene Editing focus)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eAI\/ML Scientist in Biotech\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003egRNA Design Specialist\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eBioinformatics Scientist (Genome Engineering)\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eResearch Associate in CRISPR therapeutics\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eCell Engineering Computational Scientist\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eGenomic Data Scientist\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eComputational Geneticist\u003c\/li\u003e\n\u003cli class=\"whitespace-normal break-words pl-2\"\u003eFaculty \/ Research Scholar in computational genome engineering\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eIndustries hiring for these skills:\u003c\/strong\u003e gene therapy companies, CRISPR diagnostics, agricultural biotech, pharmaceutical R\u0026amp;D, contract research organisations (CROs), cell and gene therapy startups, academic research labs, and AI-biotech crossover companies.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eWHY BIOTECNIKA?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eBiotecnika 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eThis program is part of our growing AI-in-Life-Sciences vertical, alongside our PG Diploma in AI\/ML \u0026amp; Data Science, AI for Drug Designing workshops, and bioinformatics certifications.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eCERTIFICATION\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eOn successful completion of all 45 days of training and your assigned PI-mentored project, participants receive a \u003cstrong\u003eCertificate in AI\/ML for CRISPR Genome Engineering\u003c\/strong\u003e 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 \u0026amp; 12 Months project will get a Letter of Recommendation.\u003c\/p\u003e\n\u003chr class=\"border-border-200 border-t-0.5 my-3 mx-1.5\"\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eFREQUENTLY ASKED QUESTIONS\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. I am from a pure biology background. Can I really learn AI\/ML in 45 days?\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. I already know Python. Will this still be useful?\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. How does the project phase work?\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. Can I choose my project duration?\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. Do I need a powerful computer?\u003c\/strong\u003e No. Most practicals run in Google Colab (free GPU access). A standard laptop with internet is sufficient.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. How is this different from a generic AI\/ML course?\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. What if I miss a live class?\u003c\/strong\u003e All sessions are recorded and available for lifetime access.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eQ. Will the recordings be available afterwards?\u003c\/strong\u003e Yes, lifetime access to all recordings and course material.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eGenome editing is no longer just a wet-lab discipline. The future belongs to scientists who can write the algorithms that design the experiments.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003eStarting 3rd June 2026, you can become one of them.\u003c\/p\u003e\n\u003cp class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"\u003e\u003cstrong\u003eLimited seats. Closed-door cohort. 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