frontier research deep techbayesian inferenceelectrophysiology analysisneural dynamicsspiking models
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Theoretical command
35% weight
Probe command of dynamical systems, Hodgkin-Huxley and integrate-and-fire models, dimensionality reduction (PCA, GPFA), and Bayesian or point-process methods for spike train analysis.
Evidence to listen for
Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
Knows which results are established and which are contested
Distinguishes their own contribution from the field's
Comfortable saying where the theory runs out
Five-point scoring guide
1
Poor
Recites terminology without understanding; cannot go one layer deeper.
2
Needs Improvement
Surface familiarity; conflates established results with speculation.
3
Satisfactory
Solid grasp of the core theory; thin at the frontier.
4
Very Good
Strong command; separates settled results from open questions.
5
Excellent
Derives model behaviour from first principles, states assumptions behind each method, and names where the theory breaks against real cortical data.
02
Evaluation factor
From theory to hardware or code
30% weight
Ask what they built: NEURON, Brian2, NEST simulations, PyTorch models fit to Neuropixels or calcium imaging data, and any released code or preprint.
Evidence to listen for
Has built, simulated, or run something real, not only published about it
Knows the gap between the idealised model and the actual apparatus or system
Names the practical constraint that dominates in real conditions
Can describe a result that did not match prediction
Five-point scoring guide
1
Poor
Purely theoretical; no contact with implementation.
2
Needs Improvement
Some exposure but unaware of practical constraints.
3
Satisfactory
Has implemented work; understands the main real-world limits.
4
Very Good
Strong practical record; articulate about theory-versus-reality gaps.
5
Excellent
Points to versioned repositories, simulations that reproduce published results, and pipelines other labs actually ran on their own recordings.
03
Evaluation factor
Research judgement
20% weight
Test how they chose between competing models, handled overfitting on small trial counts, and decided a hypothesis about circuit function was not worth pursuing further.
Evidence to listen for
Chooses problems by tractability and value, not novelty alone
Knows when to abandon a line of work
Reads and evaluates others' results critically
Can say what would falsify their own approach
Five-point scoring guide
1
Poor
Chases novelty; no sense of tractability or when to stop.
2
Needs Improvement
Weak problem selection; persists past the point of value.
3
Satisfactory
Reasonable judgement within a defined programme.
4
Very Good
Selects problems well and knows when to abandon a line.
5
Excellent
Describes abandoned model families with reasons, uses cross-validation and surrogate data honestly, and separates fit quality from mechanistic claims.
04
Evaluation factor
Explaining it to non-specialists
15% weight
Judge how they explain latent state or attractor findings to wet-lab experimentalists, clinicians, or grant panels who do not read differential equations.
Evidence to listen for
Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
Writes clearly
Collaborates across disciplines
Makes the case for resources in terms the audience cares about
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism.
2
Needs Improvement
Explanation is either impenetrable or hollow.
3
Satisfactory
Adequate with technical peers; less effective with lay audiences.
4
Very Good
Explains clearly to specialists and non-specialists alike.
5
Excellent
Translates model predictions into testable experiments a slice physiologist could run, without hiding behind notation or overselling correlation.
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