The tacit, transferable skills you will gain from your engineering PhD

We all know a PhD program is about doing novel research, making a small but significant dent, your dent and nobody else’s, in the frontier of human knowledge. But after you get your PhD, your intellectual and professional interests may shift and the very topic of your dissertation may or may not still be the centerpiece of your career. Because of this, some folks might question the value of doing a PhD to begin with. I argue that the tacit knowledge and skills one inevitably picks up during an engineering PhD program will yield life-long dividend. Here I explain five of these skills, and conveniently give them alliterated names: Study, Spot, Solve, Sell, Sync.

Study: learning/processing information

All engineering research starts with understanding the status-quo, the state-of-the-art, a descriptive scenario of how a problem is currently being addressed, or not at all. To gain this understanding, one needs to evaluate the extant literature on a topic of interest and/or delve into the under-the-hood mechanisms of an existing method. This experience trains your learning skills and information processing skills. Years after your PhD you will still feel the muscle memory of studying new materials and integrating comprehensive information. You will gain the attitude of “if I don’t already know something and I need to know it, I can spend some time studying it and I will learn it. ” That is some top-shelf confidence.

Spot: identifying opportunities

As a researcher, you are fundamentally an entrepreneur because you need to provide novelty and value. You will have to identify an angle, propose an idea, address a limitation, and bridge a gap — AKA spotting an opportunity and making a difference with it. Nobody can expect to achieve substantial success by copying exactly what has been done and offering no extra value. During your PhD, you have to spot the niche of problem that is yours to address. Your advisor is responsible for guiding you into a direction that both of you are passionate about, but it is your job to make something of it. Through numerous rounds of niche-finding, your acumen for new and valuable approaches gets sharpened. “Keep defining yourself until you are the best (or only one) at it.”

Solve: technical skills

Offering solutions is the actual value you create, to address the gap you spotted from studying the current landscape. Within those solutions there resides the manipulation of data, devices, tools, materials, facilities, even organizations. Therefore it is almost impossible for you to come out of an engineering PhD program without having gained marketable mathematical, computational, and engineering skills. This is why many research and tech-heavy positions in industry regard engineering PhD as equivalent to a number of years of experience. Problems in completely different domains may share the same technical approaches, making these technical skills highly transferrable, positioning you well to take on broad opportunities.

Sell: communication skills

You must not only do the work but also sell the work. The responsibility of communicating your research contribution to the world falls upon you as a PhD student and your skill level at it can make you or break you. Your PhD experience inevitably involves (1) composing and revising papers that culminate in peer-reviewed publications (otherwise you would have a hard time graduating) and (2) delivering presentations of research findings to different audiences, big and small, formal and informal, experts and laypeople. Verbal presentation skills are tested not only when you are required to give talks, but also when you unavoidably get put on the spot with the question “so what do you do?” at a professional networking event or your Saturday night party. After answering that question badly 100 times and honing your answers 101 times, you get good. Communicating value through speaking and writing is so universally required that the practice semi forced upon you during your PhD serves you long-term in many occasions.

Sync: coordination/management skills

In real life, different professional and personal tasks compete for your attention and resources so much that you will never have enough time to do everything that comes your way equally well. Therefore it is crucial to be clear about your intention, and manage your time and priorities accordingly. That includes balancing cost and benefit as well as being mindful of urgency and importance. As a PhD student, you get to practice this management skill, if not perfect it, because your pathway to a successful defense is essentially a sequence of correctly made decisions between different tasks that are of different levels of priority, consume different amounts of time, and demand each of the four skills discussed above, sometimes simultaneously.

What to put in the Discussion section?

A mentee of mine recently asked me this question as he prepares his manuscript on a novel machine learning method in human computer interaction. I suspect this is a common question for folks who are starting to write research papers for publication, especially in STEM fields. Unlike other sections such as Experiments or Results, the Discussion section can be confusing as to what content should go in. Below I list a few questions an author could ask themself — and when you have answers to these questions ready, you will have the right content to populate a nice Discussion section already.

1. Do any parts of your results seem surprising/unexpected/particularly interesting to you, good or bad? Why might that have happened?

2. Do any parts of your results agree with or contradict domain knowledge and theory? Discuss the domain knowledge and theory and how they agree or contradict. (This is where a collaborative, domain knowledge expert can chime in.)

3. If your proposed methods worked in your experiments, under what circumstances (data, participants, technology, etc.) will it be reproducible or not reproducible in another study?  

4. If your proposed methods worked in your experiments, what other applications/tasks may your methods also be good (or actually be bad) at?

This is by no means an exhaustive list but it should be a very good start to compose a strong Discussion section for your next paper.