Hiring nowNeuroscience, cognitive science and brain interfaces

Computational Neuroscience AI Researcher

Also called NeuroAI Researcher

A Computational Neuroscience AI Researcher builds and tests models of neural or behavioural data against explicit hypotheses. Their work gives a company evidence about how brains and behaviour work, and whether its models or interfaces reflect that.

What they do

They take neural or behavioural recordings and turn a loose question into a testable hypothesis. They design or refine the experiment that will answer it, clean and process the signals, and build a model that makes a specific prediction. Then they check that prediction against data the model has not seen. Much of the week goes on signal processing, comparing candidate models, and finding out why one failed. They write up results so colleagues can reproduce them, and they work with engineers who need to know which findings are solid enough to build on.

What a strong hire delivers

In the first months, a strong hire delivers a working neural or behavioural model with a clear hypothesis behind it. They produce an analysis grounded in real experiments, not only in benchmark scores. They set up an evaluation on independent measurements so the team can trust the numbers. They also leave behind readable pipelines and a short account of what the model explains and what it does not.

How you can tell they are good

Ask them to walk through one model from hypothesis to result. A strong candidate says what would have proved them wrong, how they kept test data independent, and what they changed after a failed prediction. They separate fitting the data from explaining it. Be wary of anyone who quotes only accuracy, cannot describe the recording setup, or treats noise and artefacts as someone else's problem.

Why it is hard to fill

The work sits between disciplines, so titles are inconsistent. The same person may call themselves a neuroscientist, a cognitive scientist or an ML researcher. Few people are strong at both experimental rigour and modern modelling. Academic labs hold most of them, and many are wary of company timelines. Candidates often look thin on a CV because their best work is in unpublished analysis code and experiments.

Where to find them

Look in university and institute labs, especially postdoctoral researchers in systems or cognitive neuroscience who write serious code. Biomedical engineers from brain interface and signal processing groups are a second source. So are ML researchers who moved towards neuroscience, and people in speech and audio modelling. Employers include neurotechnology firms, frontier labs with science teams, and health and devices companies.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Computational Neuroscience AI Researcher

  1. Describe a model you built from neural or behavioural data. What hypothesis did it test, and what result would have counted against it?

    A strong answer states a clear hypothesis and a falsifiable prediction, and admits a result that went against them. Worry if the hypothesis appears only after the results, or the model was chosen for fit alone.

  2. How do you keep evaluation data truly independent of the data you used to build and tune a model?

    Look for held-out sessions, subjects or conditions, and awareness of leakage through preprocessing and tuning. Worry about random splits on time series, repeated trials treated as independent, or no mention of leakage.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Computational Neuroscience AI Researcher to model neural and behavioural data and test explicit hypotheses about how brains and behaviour work. You will build models, ground them in experiments and show that they predict independent measurements. Your findings will shape what we build.

What you will do

  • Turn research questions into testable hypotheses and model designs
  • Design and run behavioural or neural experiments, or work with those who do
  • Process and clean neural and behavioural signals
  • Build and compare models, and evaluate them on independent data
  • Document analyses so others can reproduce them
  • Share findings with engineers and researchers, including their limits
  • Handle participant data with privacy in mind

What you will bring

  • Demonstrated work modelling neural or behavioural data against explicit hypotheses
  • Experience with signal processing and neural data toolchains
  • Sound experimental design and statistical judgement
  • Evidence of evaluating models on independent measurements
  • Clear, readable code and reproducible analysis
  • Ability to explain results and uncertainty to non-specialists

Nice to have

  • Experience with speech and audio modelling
  • Privacy engineering experience
  • Background in a specific scientific domain such as auditory or cognitive neuroscience

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Experimental designAdvancedChoose comparisons that identify credible effects
  • Speech and audio modellingWorkingRecognise, synthesise or analyse audio
  • Privacy engineeringWorkingLimit disclosure and unauthorised personal-data use

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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