Curriculum
- 16 Sections
- 61 Lessons
- 3 Days
Expand all sectionsCollapse all sections
- Opening & AI Myth-Busting3
- How AI Works — Architecture & Key Terms3
- Prompt Engineering for Scientists4
- 3.1COSTAR Framework: Context, Objective, Style, Tone, Audience, Response format
- 3.2Role-prompting, chain-of-thought, few-shot and zero-shot with R&D examples
- 3.3Structured output prompts: extract data tables, mechanisms, and gaps from papers
- 3.4Hands-on: write 5 research-grade prompts on your own domain in real time
- AI for Literature Review & Knowledge Synthesis4
- 4.1ChatGPT / Claude: upload multi-paper sets — extract mechanisms, identify gaps
- 4.2Perplexity AI for real-time patent landscape and prior-art review
- 4.3NotebookLM: build a queryable R&D knowledge base from internal reports
- 4.4Reduce literature-to-hypothesis time from days to under one hour — live demo
- Data Analysis, EDA & Feature Engineering4
- 5.1Excel Copilot: natural-language pivot analysis, formula generation, data narration
- 5.2Python + Pandas via no-code interface: outlier detection and feature importance
- 5.3AI-assisted exploratory data analysis on real industrial process datasets
- 5.4Identifying hidden correlations in messy experimental and sensor datasets
- AI Safety, Ethics & Governance4
- 6.1Hallucination — how it happens, how to detect it, and how to design against it
- 6.2Data confidentiality: what must never be submitted to a public LLM
- 6.3Responsible AI in regulated R&D environments: GxP, ISO, SOX considerations
- 6.4reflection exercise — 90-second synthesis shared across full cohort
- AI Workflow Automation — Copilot Studio & Claude4
- 7.1Building multi-step AI workflows without writing a single line of code
- 7.2Copilot Studio: design an agent for lab documentation and reporting automation
- 7.3Claude Skills, Cowork, Code — capabilities, constraints and R&D use cases
- 7.4Connecting triggers, actions, and automated reporting pipelines end-to-end
- Design of Experiments (DoE) & Hypothesis Generation4
- 8.1AI-assisted DoE: fractional factorial designs — 70–80% fewer experimental runs
- 8.2Bayesian optimisation and active learning for iterative R&D experimentation
- 8.3JMP + AI Assistant: GUI-based response surface modelling with AI factor selection
- 8.4Group Activity: generate a DoE matrix for your own process problem using AI
- Build & Interpret Your First ML Model — No Coding4
- 9.1Upload a process dataset; instruct AI to train a regression model end-to-end
- 9.2Interpreting variable importance, RMSE and residual plots for non-coders
- 9.3DataRobot / AutoML: automated model training, cross- validation and deployment
- 9.4Communicating ML findings to non-technical stakeholders with confidence
- Process Optimisation — Industrial Deep Dives5
- 10.1Aluminium smelting: LSTM networks for current density and anode effect prediction
- 10.2Cement kiln: ML-driven heat balance, fuel optimisation and clinker phase forecasting
- 10.3Copper flotation: computer vision for froth analysis and reagent dosing control
- 10.4Chlor-alkali: survival analysis models for ion-exchange membrane life prediction
- 10.5Carbon black: particle size and morphology prediction from furnace operating data
- Chemistry, Materials & Molecular AI2
- Build & Deploy a Working AI Agent4
- 12.1Agent architecture: perceive → reason → act → report — the agentic loop explained
- 12.2Build a literature search and summarisation agent using Claude API or Gemini
- 12.3Deploy live to Streamlit Cloud — a shareable web app within 90 minutes
- 12.4Rate limiting, cost management, and secure API key practices for R&D teams
- R&D Code Automation with Claude Code4
- 13.1Generating Python / R analysis scripts using Claude Chat and Claude Code
- 13.2Debugging, refactoring, and documenting legacy analysis code with AI assistance
- 13.3Creating automated EDA pipelines: ingest → clean → visualise → narrate
- 13.4Hands-on: write, run, and modify a full data analysis script during the session
- Rapid Experimentation & Digital R&D Framework4
- 14.1The Intelligent R&D Loop: hypothesis → AI-design → run → learn → iterate
- 14.2Reducing experimental friction: predictive pre-screening via active learning
- 14.3Institutional knowledge capture: AI tools that retain expertise before it leaves
- 14.4Measuring AI impact: leading productivity indicators and transformation metrics
- Your 90-Day AI Pilot — Implementation Roadmap4
- 15.1Framework: Problem Statement → Data Source → Success Metric → 90-Day Plan
- 15.2Participant-led pilot scoping: one concrete, named use case per attendee
- 15.3AI governance checklist for R&D organisations: data, model, risk, and audit
- 15.4Briefing structure to present your AI pilot to leadership with confidence
- Post-Quiz, Capstone Showcase & Certification4
- 16.1Post-workshop knowledge benchmark — compare against Day 2 pre-quiz score
- 16.2Certificate of Completion awarded — Applied AI for R&D, Executive Programme
- 16.3Capstone showcase: 3-minute AI pilot pitch per participant to the full cohort
- 16.4Q&A, faculty feedback, peer networking, and 30-day follow-up commitments