Category: Early Career (Page 1 of 4)

This post is related to an earlier commentary about Factors in Considering Thesis Topics in Renewable Energy. This question – appropriate selection of an advisor – comes up often, so might as well address it in parallel. The two questions are related of course, but there are some differences in how one approaches those selections. Are you choosing them? Are they choosing you? How do you maximize your contributions to the world in this stage of your life?

Getting an MSc can be seen as a transitional stage from your educational to professional careers. It’s time to polish your business development strategy, as you will have to do out there in industry when you are trying to win employment, projects and contracts. Let’s enter the stages of your search for advisors and topics. Take it in small bites.

You are emerging from your studies and found a nice position as a new engineer at a firm of your liking. The money compared to broke student life is great. Hopefully you are saving some. There’s excitement about the new phase. You finally have a chance to exercise your talents without being evaluated (or so you think, but that is a different topic). Yet it feels like you have been assigned to projects where you are not a fit. That do not play to your strengths. That are not what you envisioned before being hired.

Are you stuck in the wrong track? What should you do and when?

An old friend that teaches at a top-25 business school was expressing some dismay that some undergraduates do not grasp how profoundly statistics presented in class will influence their life. Those that fail to be mindful of the principles are at a serious disadvantage. Now, one could harp about slot machines or scratch-off lottery tickets, but since these writings are engineering-career focused, I’ll present several specific examples of how overlooking statistics can bite one in a work setting like an engineering consulting firm.

Three common situations we’ll discuss are:

1. Proposals and expected value
2. Capacity factor
3. Instrumentation uncertainty, design margins and analysis