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Two scientists walk into a bar. One steps on stage and delivers ten minutes of raucously funny stand-up comedy. The other enjoys an evening of laughter as enterprising STEM professionals share their science.
Scientists doing stand-up may sound like a joke, but it’s actually the latest innovation in science communication.
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As a graduate student, Joel Schwartz developed an immunofluorescence assay for neurotransmitter transport. To quantify his results, he needed to circle the cells in each image so the computer could measure the intensity.
By the time he graduated, Joel had circled over 10 million individual cells.
Over the years, Joel discovered a better way: he taught computers to do the repetitive, complex, and confounding parts of data analysis.
And now he trains other scientists to do the same.
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Uncertainty is a defining feature of postdoctoral training:
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If you’re an academic scientist, applying for an industry job is a bit like traveling to a foreign country.
First, there is paperwork.
Will they accept your Curriculum Vitae as is, or do you need to crunch it down into a résumé? And how on earth do you get through the screening software that filters through the 1000+ applications?
Next, there’s the language barrier.
You’ll need to communicate your qualifications in an interview that may last just a few minutes. You might describe a key experiment you designed with six controls and twelve replicates, but what the interviewer needs to hear is that you have experience in ‘quality control and quality assurance.’ Don’t expect them to make the translation.
Last, there can be culture shock when you actually get the job and start to work. There are aspects of your academic training that you will need to un-learn if you want to be successful. You can either begin the job with a sensitivity to these new cultural norms, or you can learn them the hard way…
This week, we talk with a scientist who acts as travel guide for academics who want to make the leap into industry.
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Maybe you’re in love with science, but you just can’t imagine your life as a PI. And maybe you’ve had a string of experiments fail and you’re just ready to put the entire ‘lab thing’ behind you.
You have a choice – you could leave academia and try to find your way in industry, publishing or some other career. Or you could try to revive your research in the hope that lab life will eventually improve.
But how do you know which choice is right for you?
What happens if you make a mistake?