Summary

In this episode, Neil discusses four key career questions that you should think about. He explores the pros and cons of pursuing a PhD, the path to becoming a professor, and the opportunities in the tech sector. He highlights the importance of gaining industry experience and the potential for higher salaries in the tech sector. Neil also mentions the option of dual positions, where academics work in both academia and industry. Overall, he encourages listeners to consider all the options and make informed decisions about their careers.

Takeaways

Doing a PhD can provide expertise and specialization in a specific area, but it may delay entry into the job market and result in lower initial salaries.
Becoming a professor requires a PhD and often involves postdoctoral research positions. Advancement to higher ranks, such as associate professor and full professor, requires publishing, securing funding, and taking on leadership roles.
The tech sector offers high-paying jobs and opportunities for engineers, particularly in areas like machine learning and data science. Tech companies value both academic and industry experience.
Consider the trade-offs between academia and industry, such as job security, work-life balance, and the level of freedom and autonomy.
Dual positions, where academics work in both academia and industry, are becoming more common and offer the best of both worlds.

Timestamps
00:00 Introduction
05:22 Question 1: PhD or no PhD
09:19 Question 2: How do I become a Professor?
23:10 Question 3: Academia or Industry?
31:00 Question 4: The third alternative - tech sector (Amazon, Google, META, Nvidia, Microsoft etc)
38:38 Dual Positions: Bridging the Gap Between Academia and Industry
41:00 Conclusions

Professor Tony Purnell discusses his journey from a passion for motor racing and engineering in his youth to founding and leading Pi Research, a company specializing in race car electronics. He shares his experiences at university, including a Kennedy Scholarship to MIT, and his early career in the motor racing industry. Tony also explains how Pi Research expanded into the automotive industry and eventually caught the attention of Ford, leading to the company's acquisition. His story highlights the importance of passion, perseverance, and seizing opportunities in pursuing a successful career. Tony shares his experiences in the world of Formula One, from Ford's interest in buying the team to his role in restructuring the Aero department at Jaguar. He discusses the challenges he faced and the politics and dishonesty he encountered in the industry. Tony also reflects on the stress and burnout he experienced and the difficulties he had working with Red Bull.
He highlights the contrasting views of Max Mosley and Bernie Ecclestone on the future of Formula One and the changes that occurred under Liberty Media's ownership. In this conversation, Tony discusses his experiences in Formula One and British Cycling. He talks about the challenges of managing Formula One and the difficulties faced by organizations like Toyota in adapting to the sport. He also shares his reasons for leaving the FIA, including the Max Mosley sex scandal. He highlights the innovations he contributed to Formula One, such as the introduction of adjustable ride height and the DRS system. He discusses the politics and paranoia in Formula One and the importance of working with manufacturers. He then transitions to his role in British Cycling, where he emphasizes the impact of engineering on the sport. Tony expresses his concerns about the increasing technicality of cycling and the need to balance technology with talent. He concludes by offering advice for aspiring engineers, emphasizing the importance of following dreams. Enjoy!

Florian Menter discusses his journey in the field of computational fluid dynamics (CFD) and the development of the K-Omega SST model. He shares his experiences working at NASA Ames and the collaborative environment in the CFD community. Florian also talks about his decision to return to Germany and his role in the early days of what would be become ANSYS. Florian Menter discusses the birth and development of the SST turbulence model, the challenges of transition modeling, and the future of RANS models. He also explores the potential of machine learning in CFD and shares advice for young researchers. The conversation highlights the importance of pursuing valuable ideas, keeping things simple, and envisioning the outcome of one's work.