Research assistant professor skills for your resume and career

From what I've heard (from employers), companies look for employees that they can work with. I know that sounds silly, but companies want employees that are team players and work well with other individuals and in groups. These employees also need to work independently when asked to work on a project. They need to be organized, reliable, and trustworthy. Employees also need to be able to communicate well by writing and speaking. They must be able to follow directions.

This one is tough because it depends on the position! I think what is valuable for a chemist is being knowledgeable of working with instrumentation such as chromatography and mass spectrometry. Employees that are likely more attractive for a job position than another person have had the independent experience of working with instruments and can troubleshoot problems. Employees need to be able to explain the data that they obtained from an experiment and describe what the data mean. (Data is a plural term!) Problem-solving and critical thinking is very important for scientists.

I would say getting familiar with how to process Big data and enhance GIS skills would be potential skill areas to continue to build up. There are some online courses one could l take without being enrolled in graduate/undergraduate programs to continue to build these skills (I do not have the plans offhand, but a google search would probably find some). Besides that, I would also say that trying to fine-tune communication skills about science to non-technical audiences would be good. If they are taking a gap year, use the opportunity to talk with the new people you are meeting about science in non-technical ways. See what works, what doesn't. Start building your confidence and experience doing so.

The skill sets that young graduates will need when they graduate and enter the workforce are similar to and vastly different from just 15-30 years ago. If they are working in a laboratory setting, then the standards are the same; accurate pipetting, the ability to make complex buffers, and understanding how all the necessary equipment in a lab works. However, that is not nearly enough nowadays. The equipment and instrumentation have been expanding exponentially to the point that you will be working with both expensive and complicated instruments to generate a more considerable amount of data than anyone ever thought possible. Standards for labs today will be using digital imaging devices to capture everything from microscopic images, to western blots, to automated living cell analysis using multi-well plates. Multiplexed assays for 27 to 50 to 1050 cytokines and proteins have replaced single marker ELISA. But knowing ELISA will allow you to be trained to do the multiplexed assays. Most pharmaceutical companies have a great need still for 'old-fashioned' HPLC techniques. Every student I have had in my research techniques class, that graduates and goes for a Pharma position, comes back and tells me they asked them if they could run an HPLC.
Some were even given a test to see if they understood the concept. This then leads to mass spectrometry, LCMS, MALDI-TOF, and even GCMS, and everything that has been developed around those basic techniques is now commonplace in most core facilities and Pharma. New methods for flow cytometry, FACS, are necessary for the higher throughput drug discovery types of labs. Molecular biology has evolved from simple PCR machines that could run 24 samples, just 25 years ago, to digital PCR machines that can run 384 pieces today and email the final data to you at home, while you sleep. Knowing how to calculate the PCR data is extremely critical, as it isn't intuitive, and people tend to take short cuts. Knowing how to do that will be vital. Cell culture and working with animals are still common ways to generate data in any lab, and people who have those skills will always have a job. What do all these techniques have in common? They all have evolved to the point that no one is an expert in every one of them. Labs focus and concentrate on the ones they need the most and make use of them over a long period. What a student should develop is what I call a big toolbox. Learn as many of these techniques as you can, and then use them. Understanding that these are all cyclic and that you may get rusty, or the technology will change. It doesn't matter. By being trained in any of these, it will mean that you can be prepared for other things, that you can catch up and learn and update your techniques in your toolbox. This is what any PI running a lab will be looking for, someone who can be trained, and can evolve and adapt to different technologies, know how they work and how they can be used, what the data looks like when it is working well, and what it looks like when it isn't. The people who have these skills will always be employable.

There is a greater need than ever for workers to analyze data and synthesize a reasonable idea about what it means. This means that they must understand their experiments at a deeper level than just pipetting buffers and timing reactions. They must know what is happening, and if there is a problem, first, they have a problem and then how to solve it. Bioinformatics has become one of the fastest-growing fields. The increased amount of data, whether from standard assays run in an ordinary lab or high throughput data, needs more crunching. The future researcher will not be able to get by just knowing how to use a computer stats program but will be required to understand how to run data in R or Python or whatever new data analysis package is coming next. This becomes even more critical as the data becomes more complex, i.e., 27 cytokines analyzed in 3 different tissues over three other times, from 14 different groups, 6 of which are controls, with the rest being toxin and then treatment groups and authorities. A simple two way ANOVA just doesn't cut it. For this, machine learning tools, pattern recognition, neural networks, topological data analysis (TDA), Deep Learning, etc., are becoming the norm and are being advanced and changed to give more and more substance to what the data means. Students who can operate instruments to generate data and run more complex types of analysis on this 'big data' are in great demand. Likewise, learning the computer-generated design of drugs 'in silico' is a growing field that is now required to screen tens of thousands of compounds before generating them in the lab. This will need someone who can think three-dimensionally; even though the software and advanced computers can do that, it helps if your brain is wired that way, at least a little.

Aside from instruments and complex data analysis, consider where the clinical research is headed. With COVID19, the need to quickly advance drugs from potential use to clinical application has undergone an exponential increase. Lives are being lost daily to the lack of a vaccine or medication that can attenuate to any level the impact the virus has on the human body. The future clinical researcher will need to understand how the instruments work and how tests are run, how a vaccine works, how the virus or disease manifests itself, and how to get it under control. This will only be possible if the researcher is familiar with much of what I wrote above. You won't need to be an expert on virtually everything, but you'll need to understand it so you can use it to synthesize new ideas that may be applicable in the clinical environment. COVID19 is a perfect example. One of the early struggles with this virus was how to test for it. Antibodies weren't developed for it in the very beginning, so an ELISA was out.

In contrast, PCR is one of the most sensitive methods to identify genetic material, such as viruses. So, early on, PCR primers were created that could be used to run a PCR to determine if a person had a live virus. However, the first such PCRs had high false negatives and positives. Further refinement led to the creation of PCR primer sets and protocols that allowed for a more accurate and faster test. An advantage that anyone who has been trained in biotechnology will know the basics of developing a test. If it is a PCR, then what goes into that. Suppose it is an ELISA, how it works, and what you need to set it up. Imagine a test strip similar to the one used for at-home pregnancy tests. This came about in much the same way, through experimentation and developing a way to lower the false negatives and positives, to allow a quick, 5-minute test that could determine if a particular hormone was in your urine at a stage of pregnancy when many women may not have realized there was a possibility they could be pregnant. The person entering the workforce that can think in these ways will be employable and will be able to move between jobs and continue with a very successful and enriching career.

Technical skills in addition to having a full and thorough understanding of your area of expertise will be the ability to communicate interactively via computational systems and other communication platforms. The ability to communicate (convey and express ideas) in a direct and remote setting is becoming an essential asset. The ability interact with and possibly develop simulations of experimental design and process flow paths are also technical skills that are in demand in most industries.