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About the Role

TBC is seeking a Computational Neuroscientist to 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 that connect living neural systems with modern foundation models. Your work will sit at the center of TBC’s Algorithm Discovery Platform: identifying where AI models fail, studying how biological neural networks approach related problems, and translating what we learn into usable software. 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.

Responsibilities

Design biological computing experiments, including encoding 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.
Partner with the biology team to improve culture readiness, experimental consistency, and reproducibility.
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.
Translate biological dynamics into mathematical principles, architectures, adapters, optimizers, and learning rules for AI researchers.
Test whether biologically derived principles improve generative video, world models, inference efficiency, continual learning, memory, or generalization.
Compare biological approaches against strong non-biological controls and surrogate models.
Determine which properties of the biological response are necessary for model improvement and which can be simplified for scalable software implementation.
Evaluate discoveries across model sizes, datasets, architectures, and modalities.
Help build tools for neural stimulation, real-time readout, experiment orchestration, data analysis, and rapid iteration.
Develop reusable analysis pipelines and computational tools that connect wet-lab experiments with AI-model evaluation.
Support closed-loop systems where model results inform biological experiments and biological measurements inform the next model iteration.
Contribute to TBC’s longer-term work in latent-space interfacing, neural controllability, connectome-guided learning, and real-time biological inference.
Own research workstreams from hypothesis and experimental design through analysis, validation, and technical communication.
Help define research priorities, technical milestones, and decision criteria for TBC’s neuroscience programs.
Identify scientific, statistical, and experimental risks.
Communicate findings clearly to biology, AI, engineering, product, and company leadership.
Contribute to internal documentation, research publications, technical presentations, and external scientific communications.

Requirements

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, statistical modeling, and uncertainty analysis.
Ability to design rigorous experiments and distinguish correlation from causal or mechanistic evidence.
Ability to communicate clearly and work effectively with wet-lab scientists, AI researchers, and engineers.
Strong scientific judgment, ownership, and comfort operating in a fast-moving research environment.

Skills

Python
Scientific computing
Machine learning tools
Dimensionality reduction
Latent-variable models
Neural manifolds
Dynamical-systems modeling
Encoding and decoding models
Time-series analysis
Effective-connectivity analysis
Statistical modeling
Uncertainty analysis
Closed-loop neural interfaces
Adaptive stimulation
Real-time neural decoding
Causal inference
Connectomics
Synaptic plasticity
STDP
Effective-connectivity estimation
Foundation models
Generative video
World models
Reinforcement learning
Model-representation analysis
Reservoir computing
Neuromorphic computing
Biological computing
PyTorch
JAX
Deep-learning frameworks
Reusable research infrastructure
Analysis pipelines
Internal scientific tools

Location

Remote

Work Type

Full-time

Experience Level

Ph.D. or equivalent research experience

Education Level

Ph.D.

About the Company

The Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models.
We study how biological neural networks process information, extract useful computational principles and translate those insights into software that makes modern AI models better, faster and more efficient.
Our Algorithm Discovery Platform brings together biology, computational neuroscience, AI research and software engineering to develop new algorithms, architectures and neurally-optimized software for generative video and next-generation AI infrastructure.
Today, we are commercializing neurally optimized models that run on conventional GPU and cloud infrastructure.
Longer term, we are building toward real-time biological compute, where real neurons operate alongside silicon as part of the compute stack.
Our interdisciplinary team includes researchers and engineers with experience at Apple, Johns Hopkins, Meta, MIT, Stanford and other leading institutions.