We are searching for a Computational Neuroscientist for a rapidly growing biotech company. The Computational Neuroscientist will be a part of the R&D neuroimaging team developing processing, analysis, and visualization tools for neuroscientists.
The ideal candidate will demonstrate deep understanding of statistical and machine learning concepts, particularly as they apply to processing, analyzing, and interpreting in vivo neural and behavioral datasets. A successful candidate will have excellent communication skills, with an ability to craft clear data narratives and share them via illustrative visualizations, Python notebooks, and slide deck presentations.
Requirements for the Computational Neuroscientist:
- PhD in neuroscience or related field
- Experience analyzing neural and behavioral data
- Experience manipulating, analyzing, and visualizing data in Python (e.g. NumPy, SciPy, Pandas, Matplotlib, Seaborn)
- Experience applying machine learning and/or deep learning techniques to answer neuroscience questions (e.g. using scikit-learn, statsmodels, Keras, Tensorflow).
- Experience using tools like git to version control & share code.
- Experience Scripting in Unix command line environment
- Ability to to handle multiple competing projects or priorities and pivot when needed.
Responsibilities of the Computational Neuroscientist:
- Collaborate with neuroscientists and engineers to develop innovative analyses of large scale video and time-series data, including calcium imaging and behavioral data.
- Lead external neuroscience data analysis collaborations in industry and academia.
- Perform and report exploratory analyses on novel and existing algorithms for analysis of high dimensional brain activity and animal behavior datasets.
- Assist with integration of new data processing algorithms into existing software products.
Examples of projects include:
- Develop compression and motion-correction algorithms for calcium-activity movies
- Extract single neuron activity from noisy movies containing 100s of neurons.
- Identify repeated ensembles of neural activity using clustering techniques.
- Track and classify mouse behaviors using both supervised & unsupervised approaches.
- Build machine learning models that predict behavioral states from neural data.