TBC is seeking a Computational Neuroscientist to help derive novel algorithms and model improvements for AI from understanding the dynamics of real neurons.
This is a hands-on, high-ownership role for someone who wants to help define a new field. You will work closely with wet-lab biologists, AI researchers and engineers to move from experiment to mathematical principle to model performance.
Design biological computing experiments
Design experiments that encode temporal, spatial and multimodal information into living neural cultures.
Develop stimulation and information-encoding paradigms for high-density multi-electrode array systems.
Define experimental controls, baselines and validation criteria that distinguish useful biological effects from noise or generic dynamical behavior.
Partner with the biology team to improve culture readiness, experimental consistency and reproducibility.
Analyze neural population dynamics
Analyze large-scale electrophysiological recordings from high-density MEAs and related neural-interface platforms.
Model neural population dynamics, latent spaces, neural manifolds, temporal structure, effective connectivity and state transitions.
Develop methods for decoding neural responses and identifying computationally useful spatial and temporal patterns.
Characterize how neural networks respond, adapt, learn and retain information across different stimulation conditions and time scales.
Qualification
PhDimensionality reductionLatent-variable modelsNeural manifoldsDynamical-systems modelingEncoding and decoding modelsTime-series analysisEffective-connectivity analysisExperience with causal inferenceFamiliarity with foundation modelsExperience with reservoir computingFamiliarity with PyTorch
Required
Ph.D. or equivalent research experience in computational or systems neuroscience, neural engineering, machine learning, applied mathematics, physics, statistics or a related field.
Strong background in neural-data analysis, neural population dynamics, neural coding or dynamical systems.
Experience working with electrophysiology, MEA recordings, calcium imaging, brain-computer interfaces or comparable neural datasets.
Strong programming ability in Python and experience with scientific-computing and machine-learning tools.
Experience with several of the following:
Dimensionality reduction
Latent-variable models
Neural manifolds
Dynamical-systems modeling
Encoding and decoding models
Time-series analysis
Effective-connectivity analysis
Preferred
Experience with closed-loop neural interfaces, adaptive stimulation or real-time neural decoding.
Experience with causal inference, connectomics, synaptic plasticity, STDP or effective-connectivity estimation.
Familiarity with foundation models, generative video, world models, reinforcement learning or model-representation analysis.
Experience with reservoir computing, neuromorphic computing, biological computing or other nontraditional compute substrates.
Experience connecting population-level neural dynamics to machine-learning architectures.
Familiarity with PyTorch, JAX or other modern deep-learning frameworks.
Experience building reusable research infrastructure, analysis pipelines or internal scientific tools.
Publications at leading neuroscience, neural-engineering or machine-learning venues.
