24 machining-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at University of British Columbia in canada
-
of Biomedical Engineering, please visit https://www.bme.ubc.ca/ . The computational cancer biology and pathology artificial intelligence team at the University of British Columbia’s (UBC) SBME, seeks a motivated
-
collaboration skills in interdisciplinary and community-based teams Experience with health data, mixed-methods research, evaluation frameworks, and applied AI/machine learning Understanding of ethics, equity, and
-
, Bioconductor, tidyverse, SingleCellExperiment, or related software. Experience with Python and machine learning approaches is considered an asset. APPLICATION PROCEDURE Applicants should submit: Cover letter
-
, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
-
Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow, Machine Learning Department Cooper Laboratory | Department of Orthopaedics | Faculty of Medicine (Anthony Cooper
-
of Biomedical Engineering, please visit https://www.bme.ubc.ca/ . The Stem Cell Bioengineering Laboratory focuses on the development of technologies, bioprocesses and assays for the growth and differentiation
-
, interventional effects, multiple mediation), and advanced longitudinal modelling (mixed-effects models, growth curve models, latent class trajectories) with machine learning and AI-based approaches. The Lab is
-
plans to ensure project milestones are met Experience with project management frameworks and software Strong computer skills Strong analytical, organizational, time-management, and problem-solving skills
-
and data analysis within an academic setting. More info on the research we conduct can be found here: http://molonc.bccrc.ca/ Organizational Status Working in a research software development team
-
lead an independent research program in computational oncology, centred on new quantitative and machine-learning methods that integrate single-cell genomics and spatial profiling to study cancer