This course is designed to be inclusive and interdisciplinary, welcoming students from all disciplines, however, to get the most value from the course, participants should meet the following :

• Essential :

  • • Currently enrolled in or completed an MSc, Integrated MSc-PhD, or PhD program in a STEM field (e.g., biology, physics, chemistry, mathematics, earthscience,engineering or data science)

    • Ability to sit through focused 1–2 hour webinars and interactive sessions, including presentations and live tool walkthroughs

    • Familiarity with scientific research methods and a personal or academic project that can be used for the capstone

• Recommended :

  • • Prior exposure to Python or any coding language

    • Interest in healthcare, biotechnology, or digital diagnostics

    • Experience working with any kind of scientific dataset (e.g., experimental, biosignal, imaging, or simulation-based)

• Session 1: Translational Science & Real-World Problem Mapping (2 hours)

  • • Objective: Understand how basic research can be repositioned for health and technology innovation.

      • • What is translational science? From discovery to deployment

        • Beyond Pasteur’s Quadrant: use-inspired research in academia

        • Case examples: AI for sleep biomarkers, diagnostics, and biosignal-based health platforms

        • Clinical need framing: How to define "problems worth solving"

    • Mini Exercise: Map your research interest into a translational quadrant

    • Intro to Capstone: Choose a project track (prototype or roadmap)

• Session 2: Scientific Project Management for Researchers (2 hours)

  • • Objective: Equip participants with tools to organize and deliver interdisciplinary

      • • Setting project goals, milestones,budgets and deliverables

        • Tools: Gantt charts, OKRs, Notion, Trello ,Jira

        • Scientific risk management and time estimation

        • Collaborating across disciplines: AI + biology + UX + cloud

    • Hands-on: Build a basic project plan for your capstone

    • Workshop: Peer review of individual project scopes

• Session 3: Rapid Prototyping with Modern Dev Tools (2 hours)

  • • Objective: Use simple, code-friendly tools to build interactive scientific prototypes.

      • • Tool overview: Google Colab, Replit, Cursor, and Streamlit

        • Data visualization, dashboards, ML-based calculators

        • Common use cases in health: signal classification, scoring, visual summaries

    • Hands-on: Build a Colab/Streamlit app (e.g., HRV visualizer or anomaly detector)

    • Upload and share links to get early feedback

• Session 4: Cloud Scaling with GCP, AWS & Azure (2 hours)

  • • Objective: Introduce cloud infrastructure to scale your tool or workflow

      • • Introduction to cloud: compute, storage, APIs, VMs

        • When to use GCP vs. AWS vs. Azure

        • Docker for packaging scientific tools

        • Hosting Streamlit/Flask apps on the cloud

    • Hands-on: Design a cloud architecture flowchart for your capstone

    • Tool walkthrough: Deploy a demo using Streamlit Cloud or Colab+Ngrok

• Session 5: Ethics, Regulation & Responsible Innovation (2 hours)

  • • Objective: Understand compliance and ethical frameworks for health and data-intensive research.

      • • Regulatory frameworks: FDA SaMD, CE-MDR, CDSCO

        • Patient data: HIPAA, GDPR, anonymization practices

        • Building compliant and explainable health tools

    • Exercise: Draft a basic ethical checklist for your project

    • Discussion: How deeptech companies (e.g., Flagship Pioneering) balance ambition with accountability

• Session 6: Capstone Presentations & Feedback (2 hours)

  • • Objective: Present your capstone project and receive structured feedback.

    • 5-minute presentations from each participant or team

      • • Track A: Translational Roadmap (slides + flow)

        • Track B: Live Prototype (demo + summary)

    • Instructor and peer feedback on:

      • • Clarity of problem

        • Translational value

        • Technical feasibility and scale

  • Group wrap-up discussion: post-course resources, community, and pathways forward

Participants select one of two tracks :

• Track A: Translational Roadmap

  • • Slide deck (max 6 slides): research → use case → prototype → validation → deployment

    • Includes user journey, potential collaborators, regulatory path, and cloud/data plan

• Track B: Prototype Deployment

  • • Shareable prototype (Colab, Replit, or Streamlit)

    • With 1-page technical overview (goal, dataset, methods, and next steps)

• A working knowledge of translational tools and cloud workflows

• Exposure to real-world biosignal and health data challenges

• Familiarity with compliance and data privacy frameworks (HIPAA, GDPR)

• Practical insights into venture building in deep science, with examples from clinical AI

• A structured pitch or prototype ready for refinement

• Certificate of Completion issued to participants with ≥75% attendance and capstone submission

• Certificates will be provided jointly by IISER Kolkata Alumni Association and Neurobit Inc.

• Participants who complete the course will have a direct entry into the Neurobit Ignite program

This course is not about turning scientists into startup founders. It’s about empowering scientists to think in systems, scale their ideas with technology, and engage with the world beyond the bench. I look forward to working with you and learning from your ideas as we explore the frontier between research and real-world impact.