Guest Post by Shelby L. Smith
As I sat in an audience of students listening to a panel of professional researchers and data scientists at the CNS annual meeting in San Francisco, I couldn’t help but notice two things: 1) Trainees are exceptionally worried about their futures, and 2) trainees have an army of forces rallying behind them in support of their futures. Panelists offered a wealth of insights at the the CNS Trainee Professional Development Panel last week.
Step by step
David Ziegler, the director of technology in Multimodal Biosensing at Neuroscape (UCSF), started off by telling a student to “never forget that science is incremental.” In a world where new insightful papers are published every day, and more and more individuals graduate with their Ph.D.s each semester, it can be daunting and overwhelming to look toward the future. The competition is fierce, and the stakes are increasingly high. However, Ziegler’s reminder resonated throughout the audience.
Christopher Madan at the University of Nottingham supplemented Ziegler by adding that you “can’t possibly master every skill.” There are always going to be aspects of a project that are outside of your expertise just the same as there are aspects within your specialty area. Our field needs every single one of us to advance our understanding of human cognition and the inner workings of the brain for the greater good of society. Science moves forward with every contribution, regardless of the size of that contribution or whether you end up changing paths.
All panelists suggested that students focus on a few concepts that spike their interest the most, as well as mastering a few skills associated with the methodology used in their set of interests. Additionally, they said that trainees should continually inundate themselves within the literature—in this way, more unique ideas emerge as knowledge matures. And, trainees can find their place in the scientific world. Maureen Ritchey of Boston College shared that students’ main job during their training is “to become the best at what [they] are doing.” In truly thinking deeply, asking questions, and mastering skills, students will develop their own research focus, she said.
In truly thinking deeply, asking questions, and mastering skills, students will develop their own research focus.
Charting a path
When considering postdoctoral training, panelists suggested reflecting on one’s graduate training by asking: “What am I missing?” “What wasn’t available for me to learn in graduate school?” “What skills can I add to my repertoire to complement my current research?” and “What skills can I bring to a new lab?”
One major benefit of postdoc training is that students can more closely take steps toward establishing their own research lab once they are faculty members. Panelists recommended students consider where they want to be in terms of their research for when they enter the job market at the time they are choosing their postdoc. Erika Nyhus, professor at Bowdoin College, noted that postdoc training is important for those whose goal is to get a tenure-track faculty position at a smaller, liberal arts institution as well.
All panelists who applied for academic positions after their postdoc applied in or after their second year. They suggested that students have at least one or two publications from their postdoc available to read when applying for faculty positions. But, more importantly, panelists agreed that if you can’t seem to generate a 5-year plan with specific ideas for your next research projects and different avenues you intend to go down on, then you shouldn’t be on the job market yet. When you’re able to see a clear path for your research and your ideas cohere from one to the next, then you are ready to become a principle investigator. And, when that time comes, announce your place in the scientific world.
Laura Libby, data scientist at Uber who completed her doctoral and postdoc training in cognitive neuroscience, emphasized that students need to not only know their value, but also “show [their] value.” She explained that the data science field offers both generalist and specialist positions. Generalist positions require a jack-of-all-trades, in that applicants must have a pre-specified set of analytic and programming skills in order to be a contender. Specialist positions, as well as positions posted within the academic community, are those where the company or department who posted the job ad might not know exactly what they want until you show them. They might not know who they are looking for until they find you. This is why all panelists urged students to forge their path by creating their own line of work. Make yourself known; help others follow you.
Making the transition
Transitioning from a student to the job market is competitive. Ritchey mentioned that her department receives about 200 applications for one open tenure-track faculty position. Nyhus stated that getting a position at a liberal arts college is not any easier. And, Libby added that academia is not uniquely competitive. Open positions for industry jobs also receive hundreds of applications and have a multi-part interview process.
Although the field is competitive, panelists estimated only about 25% of those who applied were suitable contenders for the position. As Nyhus reminded us, “you will graduate, and you will get a job.” There are many different paths, and sometimes switching paths is appropriate. Ritchey reaffirmed that giving up is not the same as switching paths, and switching paths is not due to failure.
If pursuing an industry position, neuroscience-specific skills are rarely a priority. Libby said there are some neuromarketing positions that use eye-tracking technology or skin conductance methods, and some pharmaceutical companies or applied healthcare positions require fMRI skills and knowledge. However, the majority of industry jobs call upon graduate students’ analytic skills for data science and software development positions.
When asked whether data science jobs were the “best of both worlds” because they include hypothesis testing without the added pressure of getting grants and tenure, etc., Libby almost immediately said “not really.” In her position, she said that she doesn’t define a problem; she defines how they solve a problem. Many data science jobs lack the theoretical development that is the core of academic jobs. More than that, though, she emphasized that students can make transitions into just about any type of industry job if they so choose—the skills we gain during our training are highly transferrable.
Science is grounded within collaboration and diverse perspectives—so, use the network of resources around you to develop large-scale and innovative projects.
Valuable odds and ends
The panel wrapped up with a few last tidbits of advice to a now less-worried and more hopeful audience of students.
Ziegler stressed that students need not be narrow-minded about their research. He agreed that students should have a focus, but he recommended that everyone expand their knowledge structure to become interdisciplinary.
Madan emphasized the importance of collaboration. He playfully said, “it’s really cool to work with other smart people,” and he’s right. Science is grounded within collaboration and diverse perspectives—so, use the network of resources around you to develop large-scale and innovative projects.
Nyhus reminded us to ignore that pang of distress saying you ‘just have to get through this part and move on.’ There’s no need to worry that you’re putting your life on hold. Rather, students should savor the process and enjoy the endless curiosity of pursuing science.
Libby made aware that your path might not be linear. Always keep an open mind and remember that you have marketable skills.
Last but not least, Ritchey closed with a quote from Nancy Kanwisher in The Quest for FFA and Where It Led: “If you can’t answer the question you love, love the question you can.”
Shelby L. Smith is a doctoral student at the University of New Hampshire and member of the CNS Trainee Association. She studies the underlying neurocognitive mechanisms of reading comprehension and mind wandering during learning using EEG, eye-tracking, and machine learning methods.
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