Research Specialist in Computational Neuroscience and Data Analysis

Research Specialist in Computational Neuroscience and Data Analysis

University of Basel

Basel, Switzerland

The position

This position will develop and maintain data-processing pipelines for large-scale neuronal and behavioral datasets, including multi-day high-frequency recordings, fluorescence biosensor imaging, and behavioral pose estimation. The specialist will integrate multimodal datasets and apply advanced statistical and machine-learning methods to uncover relationships between gene expression, neuronal activity, and behavior.

Your position

• Maintain and enhance pipelines for spike sorting, calcium imaging signal extraction, neuron tracking across recordings, and automated behavioral analysis.
• Develop efficient data storage, compression, and transfer systems suitable for large, long-duration experiments.
• Build complete, user-friendly data-analysis workflows from preprocessing through visualization, and train researchers in their use.
• Integrate multimodal datasets (activity, behavior, spatial transcriptomics, proteomics) into unified analytical frameworks.
• Apply dimensionality reduction, clustering, latent-state models, and unsupervised learning to identify high-order structure in neural and molecular data.
• Conduct statistical modeling of temporal trajectories and population dynamics across thousands of neurons.
• Work closely with microscopy and image-analysis specialists to ensure seamless interoperability across the facility.
• Provide consultation for research groups requiring guidance in analysis of high-dimensional datasets.

Your profile

• PhD in neuroscience, computational neuroscience, or related quantitative discipline (neuroscience background required).
• Strong expertise in neuronal data analysis (spike sorting, calcium imaging, long-term tracking).
• Experience with behavioral tracking and multimodal dataset integration.
• Proficiency in Python, MATLAB, scientific computing, and pipeline development.
• Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling.
• Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.