Computational Neuroscience Intern - Data Analysis and Modeling
Analyzes large-scale connectome and neurophysiology datasets, develops computational models of neural circuits, and implements data pipelines using Python/Matlab. Ideal for students in computational neuroscience or related fields with data analysis experience.
25 – 60/hr
HybridEntry levelData Science
About the role
Responsibilities
Process and analyze neurophysiology data such as electrophysiology, calcium imaging, or spike train recordings
Analyze connectomics datasets, including neuronal connectivity graphs and anatomical reconstructions
Develop computational models of neural circuits and network dynamics
Implement data analysis pipelines and visualization tools
Collaborate with researchers to interpret experimental data and generate hypotheses
Contribute to documentation, reproducible workflows, and scientific reports
Qualifications and Experience
Experience programming in Python and Matlab
Familiarity with scientific computing libraries such as NumPy, SciPy, pandas, PyTorch, TensorFlow, or similar tools
Basic understanding of neuroscience concepts, neural systems, or computational modeling
Experience working with data analysis, statistics, or machine learning workflows
Ability to work independently and communicate technical findings clearly
Preferred Qualifications
Experience with connectomics or neurophysiology datasets
Knowledge of computational neuroscience frameworks or simulators (e.g., NEST, Brian, NEURON)
Experience with machine learning or dynamical systems modeling
C++ programming experience is a strong bonus
Familiarity with Linux, Git, and high-performance computing environments
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