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Guide to Becoming a Scientist

Authors
Affiliations
University of Oxford
University of Toronto / University of Cambridge

There is no single path to becoming a neuroscientist.

Neuroscience is a vast and interdisciplinary field. Studying the brain can involve ideas and methods from many different disciplines, including:

This means that two people who both call themselves neuroscientists may work on completely different questions, use completely different techniques, and have very different skill sets.

The Many Branches of Neuroscience

Neuroscience itself can be divided into many overlapping fields. These fields study the nervous system at different scales and from different perspectives.

Molecular and cellular neuroscience studies what happens inside and between individual cells. Researchers might investigate genes, proteins, receptors, neurotransmitters, or the molecular processes that allow neurons to communicate.

Systems and behavioral neuroscience asks how neurons and neural circuits produce behavior. Researchers may work with model organisms such as mice, flies, zebrafish, or other animals. Some laboratories use experimental techniques from molecular biology and physiology, such as electrophysiology, calcium imaging, or optogenetics, which allows researchers to control the activity of particular neurons using light.

Cognitive neuroscience studies how the brain supports processes such as memory, attention, decision-making, language, emotion, and perception. Because much of this research involves humans, researchers may design behavioral experiments, collect neuroimaging data using techniques such as fMRI or EEG, recruit research participants or patients, and prepare ethics applications to ensure that their research is conducted responsibly.

Other researchers may work primarily with existing datasets rather than collecting new experimental data. They might analyze large neuroimaging datasets, genetic datasets, or maps containing millions of neurons and synapses. The 2026–2027 Connectomics Research Competition is an example of this type of research: you are using existing neuroscience datasets to ask and investigate your own scientific questions.

Computational and theoretical neuroscience uses mathematics and computation to understand the nervous system. Researchers might develop mathematical descriptions of the biophysical properties of individual neurons, simulate neural circuits, analyze patterns of activity across populations of neurons, or model networks across the entire brain.

These categories are not strict boundaries. A single research project might combine experiments, neuroimaging, molecular biology, mathematics, and computational analysis.

There Is No Single Neuroscience Skill Set

As you can probably see, becoming a neuroscientist can involve developing a very wide range of skills.

Importantly, the opportunities available to you may depend on where you study and live.

One university might have many laboratories working with mice but relatively few researchers conducting human fMRI experiments. Another might have strong neuroimaging facilities but no laboratories working with Drosophila. Some institutions may specialize in systems neuroscience and techniques such as optogenetics, while others may have strengths in neurophysiology, genetics, psychology, clinical neuroscience, or computational neuroscience.

Even laboratories studying similar questions can approach them very differently.

For example, one laboratory might study memory by manipulating particular neurons in mice. Another might study memory using human behavioral experiments and fMRI. A third might build computational models of how memories are stored in neural networks.

There is therefore no checklist of techniques that every aspiring neuroscientist needs to learn.

However, we believe that computational skills are becoming increasingly valuable across neuroscience.

Why Develop Computational Skills in Neuroscience?

Neuroscience is producing datasets at a scale that would have been difficult to imagine only a few decades ago.

At the microscopic scale, researchers can now reconstruct enormous numbers of neurons and synapses, creating detailed synapse-level connectomes.

At the macroscopic scale, large neuroimaging projects allow researchers to investigate brain structure and function across hundreds, thousands, or even tens of thousands of people.

Genomics, electrophysiology, microscopy, behavioral experiments, and many other areas of neuroscience are also producing increasingly large and complex datasets.

This means that the ability to work with data is useful far beyond laboratories that describe themselves as primarily “computational”.

Skills such as:

can be transferred across many different areas of neuroscience.

You do not necessarily need to become a computational neuroscientist. You might ultimately become an experimental neuroscientist, psychologist, physician, geneticist, or something completely different.

But as large-scale datasets become increasingly common, researchers across many areas of neuroscience will need to use, analyze, interpret, or refer to computational data at some point in their work.

This is one reason why Connectome 2026–2027 introduces you to computational approaches at two very different scales: from synapse-level connectomics to macroscale human brain networks.

Where Can a Career in Neuroscience Take You?

Doing neuroscience does not necessarily mean becoming a professor.

The knowledge and skills you develop through neuroscience can lead to many different careers.

Academia and Academic Research

One route is to conduct research at a university or research institute.

You might first join a laboratory as a volunteer, summer student, research intern, or thesis student. These are opportunities to learn how research works and contribute to an existing project.

If you decide that you want to pursue research as a career, you may eventually complete a PhD. A PhD is an advanced research degree in which you spend several years investigating a particular scientific question and learning how to conduct increasingly independent research.

After a PhD, many researchers complete one or more postdoctoral fellowships, often called “postdocs”. A postdoctoral researcher has already completed a PhD but continues working in another research group to develop further expertise, publish research, and become more independent.

Some researchers eventually establish their own laboratories as professors or principal investigators. Others move into research institutes, industry, government, education, or many other careers.

Importantly, you do not need to decide now whether you want to follow this entire path. Research experiences at each stage can help you discover what kinds of work you actually enjoy.

Research, Technical, and Administrative Roles

Laboratories also depend on many people who are not professors or graduate students.

For example, a human neuroscience laboratory might employ:

Their work might involve recruiting participants, scheduling experiments, collecting behavioral or neuroimaging data, managing clinical studies, organizing datasets, analyzing data, maintaining equipment, or helping a laboratory run from day to day.

Animal laboratories may employ research technicians who help perform experiments, prepare samples, maintain equipment, manage laboratory supplies, keep records, and care for research animals according to strict ethical and welfare requirements.

Laboratory managers may also coordinate purchasing, training, safety procedures, documentation, equipment, and the many practical tasks required to keep a research group functioning.

The qualifications required vary considerably between positions. Many paid, full-time, entry-level research assistant and technician positions require a bachelor’s degree and relevant research experience, although requirements differ between institutions and countries.

These positions can be careers in themselves, and they can also provide valuable research experience before further study.

Industry and Neuroscience-Adjacent Careers

Your neuroscience skills can also take you outside traditional academic laboratories.

For example, neuroscience and neuroimaging expertise may be useful in companies developing:

Computational skills developed through neuroscience can be especially transferable. Someone who learns Python, statistics, machine learning, data visualization, or scientific computing while studying the brain may later work as a data scientist, machine learning researcher, software engineer, or computer scientist, including in fields that have nothing to do with neuroscience.

There are also many careers connected to science that do not primarily involve conducting experiments. You might become a:

The possibilities extend far beyond the examples listed here.

Finding Your Own Path

There is no single definition of what a successful career in neuroscience should look like.

You might discover that you love working at a laboratory bench. You might prefer analyzing datasets and writing code. You might enjoy working directly with research participants or patients. You might become fascinated by mathematics and theoretical models. You might realize that you enjoy explaining science more than conducting experiments yourself.

You may also find that the opportunities immediately available to you do not perfectly match your interests. Perhaps your school or university has few neuroscience laboratories. Perhaps there are no researchers working on the particular organism or technique that interests you. Perhaps gaining research experience is difficult because of where you live, your educational stage, or other practical barriers.

That does not mean that there is nothing you can do.

Modern science provides more ways than ever to begin exploring research. Open datasets, open-source software, online scientific communities, publicly available lectures and courses, and research competitions such as Connectome 2026–2027 can allow you to start developing scientific skills even without immediate access to a traditional laboratory.

Through Connectome 2026–2027, we also hope to raise awareness of just how much you can learn and explore from the comfort of your own home. By taking advantage of open science, freely available datasets, computational tools, and online resources, you can begin learning practical research skills, asking your own scientific questions, and building projects independently.

These experiences do not completely replace being mentored by experienced researchers or working in a research laboratory. Like many other skills, learning to become a good scientist involves a form of apprenticeship: learning from people with more experience, observing how they approach scientific problems, receiving feedback, and gradually becoming a more independent researcher.

However, open and independent research experiences can give you a valuable place to start. The skills, projects, and confidence you develop can help you make the most of future research experiences and may open doors to new opportunities, collaborations, mentorship, internships, and further study.

The possibilities are enormous, and you do not need to know exactly where you are going when you begin.

In the following sections, we will introduce some general principles for finding research opportunities, developing useful skills, and making the most of your interest in science and the brain. We will then provide more specific suggestions for different stages of education, as well as strategies for students who may have limited access to universities, research laboratories, mentorship, or local research opportunities.

The goal is not to give you one path to follow. It is to help you begin finding your own.

2026–2027 Connectomics Research Competition
Tutorial 3: Data Visualization (Matplotlib)
2026–2027 Connectomics Research Competition
Start by Exploring Your Interests