Spanning the full AI Ph.D. journey, this practical guide offers clear, realistic, and action oriented advice for success. Designed as an end-to-end guide, the book leads readers from finding a research problem to experimentation, writing, conferences, and the final defense. Readers learn how to partner effectively with advisors, run productive meetings, and navigate review and rejection with confidence. The book provides guidance on responsible AI research and using LLMs effectively while safeguarding scientific integrity. The final stages of the PhD receive explicit focus, with advice on shaping publications into a coherent dissertation, preparing for the defense, and responding to examiners. This guide extends beyond graduation, exploring career paths in academia, industry, and the public sector while emphasizing transferable skills and strategic decisions. Ideal for Ph.D. students in AI and machine learning, as well as those considering starting a PhD, this book provides actionable advice that clarifies next steps and accelerates progress.

Book Description

A practical guide covering the full AI PhD journey, offering clear, realistic, action-focused advice to help students succeed.

Table of Contents

Chapter 1. What Is a PhD?

Chapter 2. Finding the Right PhD Position

Chapter 3. Start with the Problem, Not the Method

Chapter 4. Designing Experiments in AI

Chapter 5. Research Integrity and Reproducibility

Chapter 6. Responsible AI Research

Chapter 7. Meetings and Feedback

Chapter 8. Academic Writing

Chapter 9. Using Large Language Models in Your PhD

Chapter 10. Attending Conferences and Presenting Your Work

Chapter 11. The PhD Dissertation and Defense

Chapter 12. Failure, Feeling Stuck, and Moving Forward

Chapter 13. Navigating the PhD: Lessons from PhD Students

Chapter 14. Industry Internships

Chapter 15. Applying for Faculty Jobs

Chapter 16. Career Paths beyond Academia

About the Author

Stefano V. Albrecht is Associate Professor at Nanyang Technological University (NTU), Singapore, and a recipient of research fellowships from the Royal Society, the Royal Academy of Engineering, and the Alexander von Humboldt Foundation. His research has appeared in leading venues for artificial intelligence, machine learning, and robotics, including NeurIPS, ICML, ICLR, AAAI, IJCAI, UAI, AAMAS, AIJ, JAIR, JMLR, TMLR, ICRA, IROS, T-RO, RA-L. He has advised numerous Ph.D. students and is passionate about mentoring the next generation of AI researchers.