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will also have the opportunity to contribute to algorithm development, software architecture design, and software implementation. The ideal applicant for this position will have several characteristics
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Institut national de la recherche scientifique (INRS) | Varennes, Quebec | Canada | about 2 hours ago
. Responsibilities include (but not limited to): Lead the development of the NC-ARPES technique (hardware, post-processing algorithm, theory, data interpretation) Propose and perform new TR-ARPES studies of quantum
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will also have the opportunity to contribute to algorithm development, software architecture design, and software implementation. The ideal applicant for this position will have several characteristics
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self-help tools, training programs, and customer workshops to promote meaningful outcomes related to utilization of Bionano tools. Collaborate with product development teams and share critical product
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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. Familiarity with omics approaches, including genomic, transcriptomic, and metabolomic analyses. Experience with developing and applying machine learning algorithms to analyze biological data. Application
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with expertise in biology, biotechnology, computer science, microscopy and bio-engineering that is developing new microscopy hardware and new computational algorithms for the encoding and decoding
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’ histories, materials, structures, texts, and accretions over time through the application of technologies and methods developed in the natural, computational, conservation and other sciences. Examples might
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software and languages, such as SAS/R/Stata, to explore data validity, develop research variables/algorithms/flags, create analytic cohorts for each study, create sub-cohorts for trainee-led analyses, and
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, and explainability; developing unbiased algorithms and responsible data use; addressing the social impacts of AI and IT-induced biases; equitable compensation policies; combating labour discrimination