Research Assistant Machine Learning Jobs in British Columbia

Note: Applications will be accepted until 11:59 PM on the Posting End Date.

Job End Date

The expected pay (or pay range) for this position is $6,344/month.

Job Description

The Dr. Gupta Laboratory in the Department of Ophthalmology and Visual Sciences at the

University of British Columbia is seeking a full-time (100% FTE) Research Associate

(Computational Biology and Translational Bioinformatics) to support an innovative translational

research program investigating the molecular mechanisms underlying ocular and

neurodegenerative diseases. The successful candidate will bring expertise in computational

biology and bioinformatics to a multidisciplinary research team, applying advanced

computational approaches to integrate multi-omics and imaging datasets, identify candidate

disease mechanisms, therapeutic targets, and biomarkers, and support studies spanning ocular

biology, neuroscience, and translational medicine. Working closely with laboratory scientists, the

successful candidate will integrate computational analyses with experimental research and

contribute to scientific publications, the preparation of competitive grant applications, and the

advancement of the laboratory's research program.

Responsibilities

Design, implement, and manage computational biology and translational bioinformatics

analyses supporting multidisciplinary research projects.

Analyze, integrate, and interpret genomic, transcriptomic, proteomic, lipidomic, spatial

multi-omics, imaging, and other high-dimensional biological datasets.

Identify candidate molecular pathways, disease mechanisms, therapeutic targets, and

biomarkers through integrative computational analyses.

Develop reproducible computational workflows and data visualization tools to support

biological interpretation and hypothesis generation.

Integrate computational analyses with molecular, cellular, and immunological studies in

collaboration with laboratory scientists.

Develop and/or adapt machine learning and artificial intelligence approaches for the

analysis of spatial and multispectral imaging datasets, including transcriptomic,

proteomic, and lipidomic data derived from tissue specimens and cell culture models.

Prepare figures, reports, manuscripts, and presentations for scientific publications, grant

applications, and research meetings.

Contribute to the preparation of competitive grant applications and research

documentation.

Provide technical expertise, mentorship, and training in computational biology and

bioinformatics to trainees and laboratory personnel.

Work collaboratively with multidisciplinary research teams and external collaborators.

Perform other related duties as required.

Supervision Received

Works independently under the direction of Dr. Neeru Gupta.

Supervision Given

May provide mentorship and technical guidance to undergraduate and graduate students,

research assistants, and other laboratory trainees

Qualifications:

Successful applicants will have:

PhD in Computational Biology, Bioinformatics, Genomics, Systems Biology, Computer

Science, Biomedical Engineering, Molecular Biology, or a related discipline, with a

minimum of three years of relevant postdoctoral or equivalent research experience, or an

equivalent combination of education, training, and experience.

Demonstrated experience analyzing genomic, transcriptomic, proteomic, lipidomic, or

other high-dimensional biological datasets, including next-generation sequencing data.

Proficiency in R and/or Python for computational biology and bioinformatics analyses.

Strong background in statistical analysis, data integration, and scientific data visualization.

Experience developing reproducible computational workflows and managing large-scale

biological datasets.

Experience working collaboratively in a multidisciplinary biomedical research

environment.

Excellent written and verbal communication skills, with demonstrated contributions to

peer-reviewed scientific publications.

Knowledge of molecular biology, cell biology, immunology, neuroscience, or ophthalmic

research is considered an asset. Experience with single-cell genomics, spatial

transcriptomics, spatial proteomics, multi-omics integration, machine learning, artificial

intelligence, or drug discovery and development in the pharmaceutical or biotechnology

industry is considered an asset.