frontier research deep techbcielectrophysiologyneural interfacessignal processing
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 their grasp of neural signal physics: spike sorting, LFP versus EEG bandwidths, electrode impedance and drift, stimulation charge density limits, and Shannon safety criteria for chronic implants.
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
Explains electrode-tissue interface behaviour, decoder assumptions, and stimulation safety limits with numbers, citing specific literature or their own bench data.
02
Evaluation factor
From theory to hardware or code
30% weight
Ask what they actually built: closed-loop stimulators, ECoG or Utah array acquisition chains, real-time decoders in C++ or Python, firmware on ADS1299 or Intan chips.
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
Names shipped hardware or decoder code running on live neural data, with latency, channel count, and yield figures from real recordings.
03
Evaluation factor
Research judgement
20% weight
Test how they choose between invasive and non-invasive approaches, when to kill a decoder architecture, and how they handled IRB, GLP animal work, or FDA pre-submission constraints.
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 abandoning a promising approach on evidence, and shows regulatory and animal or human study constraints shaped their technical roadmap early.
04
Evaluation factor
Explaining it to non-specialists
15% weight
Assess how they brief clinicians, surgeons, and investors: explaining decoder failure modes or signal degradation without jargon, and translating neuroscience findings into product requirements.
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
Reframes neural engineering trade-offs in clinical outcome terms, with evidence of surgeons or non-technical funders acting on their explanations.
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