Connectionists: Two postdoctoral positions for the development of large-scale electrophysiological data management and sharing infrastructure

[Paul Cisek]

Partnership between the Department of Neurosciences (https://neurosciences.umontreal.ca), the Faculty of Medicine of the Université de Montréal (https://medecine.umontreal.ca) and IVADO (https://ivado.ca).

2-year positions; 75,000$/year salary

Requirements: The applicant should hold a PhD (e.g. electrical or biomedical engineering, neurosciences, or complementary field) and have research expertise in system neuroscience, programming, and data processing and analyses.

Major advances in electrophysiological recording technologies have led to an explosion in the size of datasets, as well as new challenges in processing and managing them. In particular, the advent of wireless large-scale neurophysiology and deep-learning-based pose estimation enables us to study new scientific questions on complex, unconstrained behaviors. As the pace at which we acquire data has accelerated exponentially in recent years, there is an urgent need for efficient automated pipelines to process raw data from labs into analysis-ready datasets, and to create organized structures for long-term data storage that are implemented consistently across tools and users to facilitate data sharing and complex multidimensional and multifactorial analyses. To address these challenges, several research groups at the department of neuroscience (P. Cisek, N. Dancause, B. Ebitz, A. Green, M. Perich) aim to 1) develop and deploy automated approaches to process signals recorded in our labs into analysis-ready datasets; 2) create a unified data storage and management framework to facilitate data sharing and collaborative, neuro-AI, analyses. These goals are closely aligned with those of the Union Neuroscience et Intelligence artificielle Quebec (UNIQUE; https://www.unique.quebec) center, the strategic research cluster of the Fonds de Recherche du Quebec - Nature et Technologie (FRQ-NT), the Institut de valorisation des données (IVADO; https://ivado.ca) and the Quebec Artificial Intelligence Institute (Mila; https://mila.quebec). To achieve these goals, we aim to hire two postdoctoral trainees (PDFs) seeking training opportunities at the intersection of academia and industry. The candidates should have adequate training in disciplines such as engineering, data sciences, machine-learning or neurosciences. The PDFs will advance cutting edge platforms for large-scale behavioral and neurophysiology experiments shared by several research groups on campus at the Université de Montréal. They will participate in the advancement of open source in neuroscience. Both positions will include working with unique electrophysiological datasets that will provide opportunities to develop novel or high-dimensional analytical tools that could lead to authorship on additional publications. These data sets can originate from ongoing scientific projects of one or more of the participating research labs.

1) Develop and deploy automated approaches to process signals from the labs into analysis-ready datasets.

The postdoctoral trainee will design and develop automated processing pipelines, from raw data collected in the labs to analysis-ready state, for each type of data collected. The pipeline must adapt and enhance our current tools implemented in Matlab, R or Python. This includes incorporating automated spike sorting tools such as MountainSort, KiloSort, and Full Binary Pursuit, handling local field potential signals with software like Chronux and FieldTrip, as well as creating user interfaces. The postdoctoral trainee will be responsible for creating Standard Operating Procedures and lead user training.

2) Create a unified data storage and management framework to facilitate data sharing and collaborative, neuro-AI, analyses.

The postdoctoral trainee will design and develop a robust data management platform in collaboration with researchers and staff. The platform must provide reproducible analysis automation of commonly repeated tasks and provide advanced application programming interfaces (API) that support discovery-oriented data analysis. The platform will include a central database (SQL) for storing data and metadata, provenance and versioning information, implement standardized nomenclature according to agreed ontologies, and provide an extensible suite of analysis tools (in Matlab, R or Python). The postdoctoral trainee will also be responsible for creating Standard Operating Procedures and lead user training.

For application:

Please send a short cover letter, resume and the names of 3 references to

Numa Dancause, Bsc(PT), PhD
Professor, Associate Director,
Département de Neurosciences,
Université de Montréal

Email: marlene.boutet at umontreal.ca<mailto:marlene.boutet at umontreal.ca>


Paul Cisek, PhD

Département de neurosciences
Université de Montréal
Physical: 2960 chemin de la tour, local 4117
Montréal, QC H3T 1J4 CANADA
Mailing: CP 6128 Succursale centre-ville,
Montréal, QC H3C 3J7 CANADA
e-mail: paul.cisek at umontreal.ca<mailto:paul.cisek at umontreal.ca>
Web: www.cisek.org/pavel<http://www.cisek.org/pavel>


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