In our lab, we will always strive to instill a sense of acceptance and belonging in each member of our community. It is traditional for one to refer to their lab group as their Academic ‘family’. When handled with thought and care, this analogy can be a powerful one, which celebrates diversity, unites cultures, empowers the disenfranchised, and catalyzes innovation that can change the world.

**UPDATE (for 2023 onward): The Gil Lab may accept graduate students and/or research technicians on a rolling basis. For reference, a (non-exhaustive) list of desired qualifications is noted below:

Ph.D. students

Desired Background/Qualifications:

A strong quantitative background would be helpful — specifically, experience in such areas as programming (e.g., in Python, R), code optimization/parallelization, machine learning, computer vision, differential equations, agent based modeling, and/or statistical model formulation and fitting. Experience with field-based ecological research in aquatic ecosystems would be useful but is not required. Students with backgrounds/experience in applied mathematics, computer science, and/or related fields with a strong desire to combine models and data, as well as ecology and animal behavior, are especially encouraged to reach out.

Research Technicians (applications considered on a rolling basis)

Specific duties will depend on background/training but will include such tasks as:

  • organizing, categorizing, and analyzing videos of reef fish behavior from field experiments
  • identifying fish species and labeling (with bounding boxes, or masks) each individual fish found in still frames from field videos (these labeled images are used to train neural networks to rapidly detect fish in videos)
  • using open source software (e.g., OpenCV in Python, ffmpeg in command line) to process field videos (e.g., writing scripts to automatically stabilize, cut and organize video clips of interest)
  • using a semi-automated fish movement tracking GUI to correct fish trajectories in videos
  • implementing a Python-based pipeline that utilizes structure from motion (SfM) and stereo camera footage to reconstruct fish trajectories and background coral reef habitat in 3D
  • implementing a “ray casting” algorithm to measure what each tracked fish sees over time and space, including social cues (e.g., translational or looming motion) from other nearby fish and threats
  • working on collaborative GitHub repository of data-extraction protocols for field videos of reef fish behavior
  • contributing to the development of a public-facing website that uses creative data visualizations and videos to share our pipeline and exciting findings with a broad public audience
  • assisting with the training and mentoring of others that join the lab and work on similar tasks 

Minimum qualifications:

  • Ability to work independently, remotely, and unsupervised, but also collaboratively and (if possible) in person on campus at CU Boulder
  • Strong communication skills (e.g., willingness to request clarifications, promptness in reporting issues/errors in data/protocols, etc.)
  • Keen interest in the approaches and subject matter described above and willingness to develop new computational skills

Desired qualifications:

  • Previous research experience
  • A background in coding, especially in Python
  • Interest in retaining this position for at least 8 months, potentially years


If you are interested in joining the lab as a graduate student, I highly recommend you do the following (there are some great nuggets in here for prospective undergraduates, technicians, and postdocs as well):

  1. Check out this YouTube playlist of videos I’ve been creating with friends and colleagues to help demystify this next step in your career. Some specific videos I’d suggest are:
  2. Assuming you have prior research experience (this is a must before grad school – otherwise, you have no idea what you’re getting yourself into!), it’s time to think about what types of questions, study systems, and/or approaches get you most excited — reading broadly in the scientific literature and chatting with mentors/teachers will be invaluable for this step.
  3. Read about my research (here) and by checking out some of my papers to get a sense for the kinds of research questions that interest me and the kinds of approaches I take to address these questions. Some key papers to read over include: Gil et al. Ecology (2017), Gil and Hein PNAS (2017), Gil et al. American Naturalist (2017), Hein et al. PNAS (2018), Gil et al. Ecology (2019), and Gil et al. PNAS (2020), all available here.
  4. Note: As someone who started out as a field biologist and then moved into quantitative modeling and the development of general theory, I am particularly keen to recruit students on either end (and anywhere in the middle) of the empirical-theoretical spectrum, but that are unified by a strong interest in linking extensive field datasets and mathematical/computational models. I’ve found the data + modeling approach to be a very powerful one, and so if you’re a field biologist, I hope you’re interested in developing quantitative skills, and if you’re a theoretician, I hope you’re interested in rooting your models to real-world systems.
  5. Think about, conceive, and write out one or more research ideas that you might be interested in pursuing and that seem to align, at least tangentially, with my expertise and/or research foci. Do you have the skeleton of a ‘dream project’ in mind?
  6. Send me an inquiry email (give this blog post from Dr. Jacquelyn Gill [no relation] a read to help craft it). In addition to sharing your research experience and interests, please also let me know if you have any interest/experience in science communication and/or fostering justice, equity, diversity, and inclusion (JEDI) in science. This a focus that I’ve invested a lot of time and energy into (read about that here) and that our lab group will often tackle in tandem with our research program, because: 1) I’ve found these activities to be synergistic and for life to be more fulfilling with both, and 2) well, we must diversify access to science if we want to use science to help the world. Email me at: michael.gil [at], and use the subject line “Prospective Graduate Student”.
  7. If you don’t hear back from me in 2 weeks, send me a reminder, and then repeat (I’ll do my best to get back to you quickly the first time, but, frankly, my email inbox is a bit crazy). I promise that you’re not being rude by re-emailing — you’re being persistent, which is an essential attribute in the science game. 🙂
  8. Finally, for fun (and utility for me): add this statement somewhere in your inquiry email to let me know that you read through all of this before reaching out to me: “I hope to see you on the other side”.

If, following your inquiry, you and I hit it off, the next steps will include formally applying to graduate school in the CU Boulder Department of Ecology & Evolutionary Biology (EBIO) by the application deadline of December 1st. I’d recommend giving this blog post from Dr. Meghan Duffy a read before you do this. A few other things to note, in considering whether to apply:

  • We have eliminated the GRE – so, you don’t need to take this costly, cumbersome, and not particularly useful (e.g.) exam to get admitted into our program.
  • If application costs are an issue, please let me know before deciding not to apply. Also, we are taking steps in EBIO to reduce or eliminate application fees for those who are economically disadvantaged.
  • EBIO is not just chuck-full of great scientists that run the gamut of ecology, evolutionary biology and behavior, but the departmental culture is the most progressive I’ve encountered at a leading research university. The department (and, actually, the university as a whole) seems refreshingly committed to not just leading the world in science but also taking actions to foster JEDI — something we desperately need to improve upon in STEM fields.
  • Our lab, EBIO, and our scientific discipline as a whole will benefit from a diversity of perspectives. So, don’t count yourself out because you don’t appear to ‘fit the mold’ of how you perceive a scientist is supposed to be or act — the truth is that any mold that exists is from a legacy of inequality, and as we continue to acknowledge and strategically mitigate barriers to entry in STEM, I believe the premium on diversity in the STEM professional workforce will continue to rise. In short, if you think you don’t belong, you probably do, and maybe more so than you can imagine.

Finally, to end on a personal note: I have walked an unlikely path to become a professor in science. I am the son of an immigrant from Argentina, I was raised working class by a single mother in an oil town in Texas, and I attended underfunded public K-12 schools that did not have the bandwidth to open up my world to the possibilities that I now know exist (but are, unfortunately, still largely restricted to a small subset of society). I got here through hard work and chance run-ins with good people. I’ve felt like an imposter at each stage of my career, especially in graduate school. It’s been a struggle — a struggle that is far from unique to me.

But I see the tide turning. I am part of a community of like-minded scientists that want, as badly as I do, to see our profession become inclusive and, in doing so, empowered. I am thrilled to be in a position to lead a lab group, but we cannot, in good conscience, simply do research in a silo. We need to also take action to bridge the gap between science and the diverse, mass public. I am deeply motivated to continue to contribute to this effort, which, considering the unprecedented global threats we now face (e.g., climate change, pandemics), perhaps defines the grandest challenge of our time.

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