Getting Your First Research Experience? - 2026–2027 Connectomics Research Competition

Finding Your First Research Experience¶

The best strategy for finding your first research experience will vary depending on your circumstances.

Are you a high school student or an undergraduate? Are you looking for a laboratory at your own university or somewhere else? Are you hoping to volunteer, complete a thesis, or find a paid summer internship?

Different institutions also have different policies about who can work or volunteer in their laboratories.

We will give some more specific advice later. But first, there are a few general principles that I think are useful to understand.

Before we begin, I also want to emphasize that much of the advice in this section comes from personal experiences and anecdotes. Different professors, laboratories, fields, institutions, and countries work differently. There is no formula that guarantees you a research position.

What I hope to do instead is help you understand how things might look from the other side: why might a research group decide to take on a student, and what can you do to make that decision easier?

Understand That Training a Student Takes Time¶

As a trainee entering your first research experience, there is something slightly counterintuitive that you should understand.

No matter how much you prepare beforehand—how many papers you read, how much data analysis you learn, or how much background knowledge you develop—you will probably still need to be trained substantially from scratch when you join a research group.

That training might come from the principal investigator (PI), meaning the professor or researcher who leads the research group, but very often it will come from a graduate student, postdoctoral researcher, research assistant, technician, or another member of the laboratory.

Why?

Because every laboratory works differently.

A laboratory might use an experimental setup that you have never encountered before. It might use specialized equipment or software. Its analysis pipeline might be different from what you learned independently. The group might also be pursuing a research direction that is difficult to fully understand simply by reading its previous papers unless you have been in the field for a very long time.

I know this sounds slightly counterintuitive given everything we just told you about reading papers, developing skills, and leveraging open science.

But understanding this is important.

Training a new student can require a significant investment of time from a research group. Someone has to teach you, answer your questions, check your work, troubleshoot your mistakes, and gradually help you become independent.

This is one reason why laboratories can be selective about which students they take.

So what might make someone choose you?

Show That You Can Commit¶

Here is another slightly counterintuitive example.

Suppose you are an undergraduate student. Professors might actually prefer taking a second-year student over a third-year or final-year student, even if the more senior student has more knowledge and experience.

Why?

The second-year student might be able to stay in the laboratory for several years.

If the professor and graduate students spend months training that student, the student might eventually complete a summer project, continue working part-time, complete a thesis in the laboratory, and contribute to the group for another year or two.

If you join during your final year, on the other hand, you might graduate and leave after 8–12 months.

Of course, there is enormous variation between laboratories, and this certainly does not mean that senior students should not apply. The point is simply that your existing experience is not the only thing a research group considers.

One of the most important things you can demonstrate is commitment.

Saying that you are passionate about neuroscience is helpful, but passion alone may not tell the professor whether you will still be there six months later.

A more concrete statement might be:

“I would like to volunteer in the lab from September to April for 10 hours a week to learn the relevant skills, and I would be excited to apply for a summer research award and continue as a thesis student next year.”

Now the professor can see a potential timeline.

I know this example assumes that you are an undergraduate at an institution with research courses, thesis programs, and summer awards. This example also assumes that you can take time off to volunteer. Your circumstances might be completely different.

The broader point is this: be concrete about how long you can commit and what you hope the experience could develop into.

In fact, because commitment matters, some professors may not recruit volunteers at all. They may prefer students who have a part-time employment contract, hold a summer research award, or are completing a formal research course or thesis.

These arrangements provide some structure. A student who is being paid, receiving an award, or earning course credit may find it easier to make a reliable time commitment than someone informally volunteering whenever they have time.

This also helps explain why it can be harder for a professor to accept a student from another institution or another country.

There may be questions about funding, insurance, institutional affiliation, visas, the right to work, access to facilities, or whether the student is even permitted to volunteer. Sometimes these problems can be addressed through research awards, exchange programs, or other formal arrangements. However, since the logistics can be much more complicated, they may inevitably prefer committed students from their own institutions.

Apply Early¶

Once you understand the importance of commitment and logistics, the next piece of advice makes more sense:

Apply early.

Very early.

Suppose you want a summer research internship from May to August 2027, and applications for the relevant summer research award close around January 2027.

Contacting potential PIs in August 2026 is not necessarily too early.

In fact, if you want to find a paid internship or apply together for a research award, starting this early can be very helpful. You have time to identify laboratories, contact researchers, discuss possible projects, understand the funding options, and prepare an application before the deadline.

You might be thinking:

“But I don’t know anything yet! Their research is really difficult. What am I supposed to say?”

Remember what we just discussed.

Many professors do not expect an undergraduate applying for their first research experience to already be an expert in the field. They know that they will need to train you.

And trying to write a long, incredibly sophisticated email demonstrating that you understand everything about their research can take an enormous amount of time.

Your first email often does not need to be that complicated.

Your First Email Can Be Short¶

Let’s say it is mid-August 2026.

You are an undergraduate interested in memory and fMRI, and you find a professor at your university whose research interests you.

Your email could look something like this:

Dear Prof. [Name],
I’m Sapolnach, an undergraduate student studying neuroscience at the University of Toronto.
I would like to apply for the [XYZ Summer Research Award/Internship] to work in your lab during the award period from May to August 2027, and I would be excited to continue as a thesis student in the 2027–2028 academic year.
I am excited by your research using fMRI to study how our brain, and the hippocampus in particular, binds different types of stimuli to form coherent memory representations. [assuming they do this kind of research]
I also have a basic programming background in Python. Through Connectome 2026–2027 (a connectomics research competition), I have explored the Mind-Brain-Body fMRI dataset of 200+ healthy participants and used Nilearn and NetworkX to investigate how brain networks may differ with age, which I hope would help me grasp neuroimaging analysis in your lab more quickly.
I have attached my transcript and CV to this email.
Best,
Sapolnach

As you can see, short and concise.

And the Connectome 2026–2027 sentence is optional. Although you might have no previous research experience at all, there is usually still something relevant you can mention. Perhaps you have taken a statistics course. Perhaps you have started learning Python. Perhaps you read an introductory psychology textbook and have begun reading papers about memory. Perhaps you completed a relevant class project.

So, in very simple terms, the email contains:

A short introduction: who are you?
A concrete timeline or plan: when do you want to join, for how long, and is there a program, award, thesis, or course involved?
One line about their research: what specifically interests you about their work? Reading their lab websites or faculty profile may help.
If relevant, one to two lines about your existing skills or knowledge: is there anything that might help you get started?
Your CV and transcript, if appropriate.

For an undergraduate applying to laboratories within their own institution, sending this kind of concise, targeted email to many relevant laboratories can be a very reasonable strategy.

You do not necessarily need to spend 10 hours writing every initial email.

But what if the situation is more difficult?

What If You Are Applying Somewhere Much Harder to Access?¶

Suppose you want to join a research institution abroad.

Or a laboratory at another university.

Or perhaps an extremely large or competitive laboratory. Let’s say, for the sake of argument, a Nobel Prize winner’s lab.

Or perhaps you are a high school student.

Now the commitment argument becomes more difficult.

You cannot necessarily say:

“Train me now and I will stay for three years, complete a thesis with you, and continue working in your lab.”

There may also be significant logistical and administrative barriers to bringing you into the laboratory, which makes the professor prefer students from their own institutions.

So what do you do?

If you are an undergraduate, I would generally suggest first making sure that you have properly explored opportunities at your own institution and other institutions accessible to you.

If you are a high school student, or an undergraduate who has already explored local options and now wants to pursue something further away, then you may need to do more to demonstrate why taking you on would be worthwhile. This is very common!

There is no perfect answer here.

But there are several things that can improve your chances.

First, Look for Formal Routes¶

Where possible, particularly if you are an undergraduate hoping to work abroad, look for formal exchange programs, research awards, and international internship schemes.

For example, Canada has the Mitacs Globalink Research Internship:

https://www.mitacs.ca/our-programs/globalink-research-internship-students/

Different countries, universities, and funding agencies have their own programs.

Do your research.

Sometimes the laboratory you are contacting may not even know about a particular program available to you. If you can tell a professor, “There is an exchange program that would fund my visit and provides a formal route for me to work at your institution,” you have already removed some of the logistical uncertainty for them.

But what if no such program exists?

What if you are asking a laboratory to take you on despite having no obvious institutional connection?

Then you may need to demonstrate more clearly that you could become a good, increasingly independent trainee and that you could eventually add meaningful value to the research group.

This is where everything we discussed in the previous sections starts coming together.

Use What You Have Learned to Go Deeper¶

Two things can be particularly useful here:

Reading scientific papers in the way we described earlier, especially papers / topics directly related to the laboratory you want to join.
Leveraging open science and developing computational skills that allow you to work with real scientific data and tools independently.

The second point can be especially useful for laboratories that run experiments but also need people who can work with increasingly large datasets and computational resources. This is common in fields such as neuroimaging, genetics, bioinformatics, and many other areas of neuroscience.

And computational skills can go beyond analyzing existing datasets.

For example, if you are interested in human behavioral research, you could learn how researchers program behavioral experiments using open tools such as Pavlovia:

https://pavlovia.org/

The broader idea is to ask:

What does this lab actually do, and what could I start learning to do now?

So how do we figure that out?

Go Beyond the Lab Website¶

The laboratory website is usually a good place to start.

It can tell you the major research themes of the group, the techniques they use, who works there, and sometimes which projects are currently underway.

But laboratory websites are not always up to date.

So the next place I might look is Google Scholar.

And then you encounter another problem.

The professor has published way too many papers.

Where do you even start?

Let’s take one of the faculty advisors for Connectome 2026–2027 as an example: Professor Morten L. Kringelbach at Oxford.

If you look through his recent publications and sort by year, you will see a very large number of papers published across 2025 and 2026.

So how do we figure out what his group’s current research directions might be?

One useful clue is authorship position.

Depending on the conventions of the field, the first author often made a major contribution to carrying out the work, while the last author is often the senior researcher or PI who supervised the project. The last author may also be the corresponding author, although authorship conventions vary between fields and papers.

So, rather than trying to read every publication, I might first look for recent papers where the researcher I am interested in is the first or last author.

For example, among Prof. Kringelbach’s recent papers, we might encounter titles such as:

The turbulent brain: Modeling vortex interactions for understanding human cognition
A canary in the mind: A single baseline brain scan predicts adolescent depression and anxiety one year later
Competitive interactions shape mammalian brain network dynamics and computation

Now I start looking for patterns.

The first paper suggests an interest in modeling the brain using ideas that sound quite related to physics, such as turbulence and vortex interactions.

The Canary in the Mind paper uses a large-scale dataset called HCP-BANDA to investigate the prediction of depression and anxiety using functional connectivity—something conceptually related to the kind of large-scale neuroimaging analysis you encounter in Connectome 2026–2027. The abstract also mentions a whole-brain generative model.

Then I look at Competitive interactions shape mammalian brain network dynamics and computation. Its abstract says:

“Here we use computational whole-brain modeling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome.”

Okay.

Now I have something much more concrete to work with.

This group seems interested in whole-brain modeling, brain network dynamics, connectomes, and the use of ideas from physics and computation to understand the brain.

If you look further into his lab websites, you will see that this professor also has written reviews such as The Thermodynamics of Mind or a book “Whole-brain modeling. Cartography of the dynamics of mind.”

If I were trying to join this laboratory without any existing connection, I might now look at their GitHub repositories (often mentioned toward the end of their research papers) to see what code did they use to produce the figures and their results.

So as you read their papers, think of the following questions:

What methods do they use?
What exactly is whole-brain modeling?
Is their code publicly available?
Can I get it running?
Can I reproduce one of their analyses?
Could I apply a similar method to a large-scale dataset that I already have access to?

Then, rather than simply emailing:

“I am passionate about neuroscience and would love to work in your lab.”

I might eventually be able to email the professor and say:

“I read your recent work on whole-brain modeling and applied part of the approach to another public dataset to answer XYZ research question (share your GitHub repository). I would love to explore within the context of “XYZ neuropsychiatry topics” in your lab.”

Or perhaps I come from a strong physics background. Then I might read their work and start thinking:

Can something I already understand about physics help me understand the brain?

Now we have a much more interesting conversation.

I think you are much more likely to get someone’s attention this way, even if you have no prior formal research experience.

I know this is hard.

But if this is genuinely your dream institution, your dream laboratory, or a research question that you cannot stop thinking about, it may be worth trying.

Postdocs and Grad Students¶

There is one final practical point.

The PI may lead the laboratory, but they are often not the person who would directly supervise you every day.

Very often, your actual mentor will be a graduate student or postdoctoral researcher.

So if you have carefully read a paper that interests you, look at the first author.

Perhaps the first author is a postdoctoral fellow in the laboratory. Perhaps they are a PhD student working directly on the question that interests you.

You can sometimes send a similar email to them.

Graduate students and postdocs often have many questions they would like to investigate and only so much time to investigate them. If they think you could become a useful and reliable extra pair of hands, they may be interested in mentoring you.

Again, there is no guarantee.

But now you are no longer simply asking:

“Can you give me research experience?”

You are beginning to show:

“I understand something about what you are trying to do. I have started developing the skills to contribute. I am willing to learn. And here is how I might fit into your research.”

That is a very different request.

Beyond Emailing¶

Throughout this section, we have focused quite a lot on emailing researchers because it is one of the most direct ways to approach a laboratory.

But email is certainly not the only way to connect with researchers or find opportunities.

There are also many other ways to connect with researchers beyond email. You might meet them through conferences, seminars, workshops, journal clubs, summer schools, or student research events, or, if you are at the same institution, through office hours, courses, or departmental events. The same general principles we discussed above still apply: show that you are genuinely interested in their research, demonstrate that you are willing and able to commit, and, where possible, show the knowledge or skills you have already developed that could help you contribute. Sometimes a thoughtful conversation about their work can be the beginning of a research opportunity too.

Dealing With Rejection¶

Finally, expect that you will sometimes be rejected or simply receive no response. This is normal, and there are many possible reasons: a lab may have no space or funding, the researcher may be too busy to supervise another student, or the timing may simply not work.

If you receive no response, it is also completely reasonable to follow up once or twice. For example, you might send a short and polite follow-up after around two weeks. If you still hear nothing, you could potentially try again a month or two later—especially if something has changed in the meantime. Perhaps you have completed a project, learned a new skill, read more of their work, or now have something more concrete that you could contribute.

At the same time, do not wait around for one particular lab. Reach out to multiple labs, continue reading and developing your skills, and keep applying. Even an unsuccessful application can help you refine your interests, improve how you approach researchers, and become better prepared for the next opportunity.

Leveraging Connectome 2026–2027 and Public Datasets¶

With Connectome 2026–2027, we are trying to open the door to two very exciting areas that span very different scales of neuroscience: from synapse-level connectomics to macroscale connectomics of the human brain.

But we hope that what you take from the competition goes beyond connectomics itself.

We hope that you learn how to approach a scientific question, read the literature around it, teach yourself unfamiliar concepts, develop computational skills, work with open scientific data, and eventually investigate something that you find interesting.

And we hope that, no matter where you are or what stage of your education or career you are at, you can use these skills to pursue the science you want to do and perhaps open doors to opportunities that you had never thought were possible.