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The Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models. The company studies how biological neural networks process information, extracts computational principles, and translates insights into software that makes modern AI models better, faster, and more efficient.
As a Computational Neuroscientist, you will help derive novel algorithms and model improvements for AI from understanding the dynamics of real neurons. You will work across computational neuroscience, biology, and machine learning to design experiments, analyze large-scale neural recordings, and build models connecting living neural systems with modern foundation models.
Key responsibilities include:
**Design biological computing experiments**: Create 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.
**Analyze neural population dynamics**: Analyze large-scale electrophysiological recordings from high-density MEAs. 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 patterns. Characterize how neural networks respond, adapt, learn, and retain information across different stimulation conditions.
**Translate biology into AI systems**: Work with AI researchers to convert neural dynamics into mathematical principles, architectures, adapters, optimizers, and learning rules. Test whether biologically derived principles improve generative video, world models, inference efficiency, continual learning, memory, or generalization. Compare biological approaches against strong controls and surrogate models.
**Build closed-loop research infrastructure**: Help develop tools for neural stimulation, real-time readout, experiment orchestration, data analysis, and rapid iteration. Develop reusable analysis pipelines connecting wet-lab experiments with AI-model evaluation. Support closed-loop systems where model results inform biological experiments.
**Shape research strategy**: Own research workstreams from hypothesis and experimental design through analysis and validation. Help define research priorities and technical milestones. Identify scientific and experimental risks early. Communicate findings to biology, AI, engineering, product, and leadership teams.
Success means neural experiments produce consistent, interpretable population-level data; biological observations convert into testable computational hypotheses; validated neural principles become software producing measurable AI improvements; results hold against strong controls and ablations; and experimental pipelines accelerate the team's movement from question to evidence.
