Career & Networking

AI projects for early talent

Hands-on projects pair an experienced mentor with early-career talent. Each collaboration is scoped to finish inside its timeframe and produces concrete statistical deliverables, including a visualization dashboard and documented evaluation results.

Choose a focused four-week sprint or a phased 10–12 week collaboration. Project topics are shaped around responsible, high-value uses of AI across clinical development.

2 Project pathways
4 Weeks · Short-term
10–12 Weeks · Long-term
TBD Mentors & students

Each project needs a mentor and one or more students. Participants and final project scopes will be matched through the PharmaDS mentorship program.

AI governance · Clinical development

Project pathways

Expand either pathway to see the proposed scope, deliverables, plan, and skills participants can develop.

AI readiness and opportunity assessment Short-term · 4 weeks Early career

The gap. Life sciences teams need a transparent way to identify AI opportunities that are valuable, feasible, and appropriate for their clinical and operational context.

What you build. A structured assessment that compares candidate AI use cases using evidence, feasibility, impact, readiness, and governance considerations.

Deliverables

  • Prioritized AI opportunity matrix
  • Visualization dashboard or interactive prototype
  • Documented evaluation criteria, results, and recommendations

Plan · 4 weeks

  1. Define the clinical-development context and decision criteria.
  2. Inventory and structure candidate AI use cases.
  3. Score, compare, and visualize the opportunities.
  4. Document findings and present a prioritized recommendation.

Industry impact

  • Focuses effort on AI opportunities with measurable value.
  • Makes prioritization assumptions visible and reviewable.
  • Connects innovation decisions with responsible governance.

Skills you gain

AI strategy Evaluation design Data visualization
Mentor To be identified
Student(s) To be identified
Responsible AI governance roadmap Long-term · 10–12 weeks Early career

The gap. Moving an AI concept into clinical-development practice requires governance, validation, documentation, and oversight that fit the use case and its risks.

What you build. A phased roadmap and practical toolkit for governing an AI use case from initial evaluation through implementation and ongoing review.

Deliverables

  • Phased responsible-AI governance roadmap
  • Validation and documentation checklist or toolkit
  • Progress dashboard and documented evaluation results

Plan · 10–12 weeks

  1. Define the use case, stakeholders, intended use, and risk profile.
  2. Map governance, validation, documentation, and oversight needs.
  3. Develop the roadmap, templates, and monitoring approach.
  4. Test the framework against a representative scenario.
  5. Refine, document, and present the final recommendations.

Industry impact

  • Turns responsible-AI principles into an actionable workflow.
  • Improves consistency, traceability, and readiness for review.
  • Supports scalable adoption across clinical-development teams.

Skills you gain

AI governance Model validation Technical documentation
Mentor To be identified
Student(s) To be identified

Want to take on an AI project?

Join the PharmaDS mentorship program and tell us which project pathway best fits your goals.

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