About the Client
Our client is developing advanced interfaces at the intersection of neuroscience and intelligent systems. Their work focuses on building high-fidelity models of whole-brain function to deepen our understanding of cognition, behaviour, and biological computation.
They operate where neuroengineering, AI, and systems design converge — creating technologies that tightly link sensory processing, neural computation, and behaviour. A key long-term ambition is to enable data-driven alternatives to traditional animal testing by producing accurate predictive models of neural activity, while contributing to a future where advances in AI improve human insight and agency.
The Role
We are partnering with our client to identify a Neuroscientist with hands-on expertise in in vivo electrophysiology and implantable neural recording systems. This individual will play a critical role in shaping and executing the experimental foundation that supports the client’s development of interpretable, brain-inspired technologies.
This is a highly cross-disciplinary position at the interface of experimental neuroscience, engineering, and computational modelling.
Key Responsibilities
- Experimental Design & Delivery
- Lead in vivo electrophysiological studies using chronic and/or acute multi-channel implants
- Develop and optimise protocols for neural recording, stimulation, and behavioural coupling
- Validate new implant designs and refine surgical workflows alongside engineering teams
- Record, curate, and analyse electrophysiological data across multiple brain regions
- Implant & Systems Integration
- Collaborate with hardware engineers on neural probe, headstage, and implant design
- Integrate new sensors, amplifiers, and stimulation modules into experimental setups
- Manage recording rigs, acquisition software, and associated data pipelines
- Data Analysis & Interpretation
- Process large-scale datasets using Python, MATLAB, or related analytical frameworks
- Implement spike sorting, LFP analyses, and time–frequency signal processing
- Work closely with computational neuroscientists to model and interpret neural activity
- Cross-Team Collaboration
- Partner with AI, embedded systems, and hardware teams to connect biological data with computational architectures
- Support automation of experiments and behavioural monitoring
- Contribute to strategic research design discussions, influencing both scientific and engineering roadmaps
Candidate Profile
The ideal candidate is a hands-on experimentalist with strong analytical skills, comfortable working across wet-lab and computational environments. They should be motivated by high-impact, long-term scientific challenges and thrive in cross-functional teams.
You will bring:
- Extensive experience with in vivo electrophysiology
- Strong problem-solving instincts and experimental rigour
- A collaborative mindset and an interest in bridging neuroscience with technology development
- Commitment to reproducibility, documentation, and scientific integrity
Requirements
- PhD (or comparable experience) in Neuroscience, Neurophysiology, Biomedical Engineering, or related field
- At least 1–2 years of hands-on experience with electrophysiological recordings in animal models
- Proficiency with neural acquisition systems (e.g., Intan, Open Ephys, Neuropixels, Plexon)
- Practical experience in signal processing, spike sorting, and time-series analysis
- Strong Python, MATLAB, or equivalent technical skills
- Familiarity with stereotaxic surgery, chronic implants, or neural stimulation systems
- Solid grounding in neuroanatomy and experimental design
Preferred Experience:
- Prior work developing or adapting neural recording hardware
- Exposure to optogenetics, calcium imaging, or multimodal neurotech
- Experience in research environments spanning both experimentation and computation
- Publications or open-source projects in systems neuroscience or neurophysiology
