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and diplomas issued abroad, please see Degree equivalency Experience Proven experience in multi-element alloy (MEA) computational design using AI/ML approaches, and related finite element modelling
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structures, including the use of finite element modelling and simulation tools. Experience with data-driven design optimization would be an asset; Direct hands-on experience in advanced composite manufacturing
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structure for integration into cardiac catheters. Conduct finite element analysis (FEA) to simulate force/strain responses under clinical conditions. Assist in the setup and execution of in-vitro and in-vivo
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: Our group developed a new method, smol-seq, that uses DNA sequencing to measure metabolites and drugs. This harnesses the incredible power of DNA sequencing for metabolomics. It also allows metabolomics
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Optimized Tissue Culture and Single-Cell Omics Digital Composite Manufacturing Multi-Element Alloys Resistant to Hydrogen Graph based inverse reinforcement learning (IRL) framework for analyzing and
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methods to diagnose and monitor childhood cancers. This may include the use of ultra-rapid RNA-sequencing for real-time diagnostics and analysis of circulating nucleic acids to monitor drug resistance
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Qualifications PhD in public health or related field obtained within the last five years. Proven ability to thrive in a collaborative, mixed-methods research environment. Strong personal motivation, self
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or related field obtained within the last five years. Proven ability to thrive in a collaborative, mixed-methods research environment. Strong personal motivation, self-management, and detail-oriented approach
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economics methods (e.g. resource utilization and cost analysis, decision and outcomes modelling) to patient-level data from cohort studies, linked administrative datasets, or other primary data collection
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Experience with both qualitative and quantitative research methods Strong oral communication skills Ability to work independently as well as part of a team Experience with statistical software (e.g., SPSS