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(NGS), qPCR-based molecular assays for microbial quantification and aptamer validation, and experimental support for aptamer discovery targeting environmental microorganisms listed in the US Pharmacopeia
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technologies for steel fabrication and construction ii. Experimental investigation of high-strength aluminium alloy structures iii. Development and testing of lightweight and long-span earth retaining and
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of the CFI Project (https://www.pub.gov.sg/-/media/PUB/Resources/Press-Releases/2024/06/Annex-A_Tranche-2-Research-Projects-Awarded-Under-CFI-Singapore.pdf ). The primary role involves developing and
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. The Research Fellow/Engineer will be a key member of the research team focused on developing a framework for soil conditioning in Singapore soils through a series of experimental investigations. Additionally
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mechanisms. iii. Develop and implement measurement protocols, experimental setups, and data acquisition procedures for airborne and structure-borne noise studies. iv. Analyse measurement data using specialised
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) to address the relevant research questions. Perform statistical analysis on survey/experimental data, and conduct thematic analysis on interview transcripts and open-ended survey responses. To lead and report
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architecture, component selection, and experimental setup. iii. Conduct lab and site tests to evaluate the performance of NQR prototype. iv. Generate design documents and test documents at every stage
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that are relevant to industry demands while working on research projects in SIT. The researcher will be part of the team of the CFI Project (https://www.pub.gov.sg/-/media/PUB/Resources/Press-Releases/2024/06/Annex
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with experimental data Assists the PI in drafting of reports, conference proceedings and journal articles based on the outcome of the projects. Prepares and shares fortnightly report of results from
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platforms Design and execute surveys and interviews to collect data (including participant recruitment) to address the relevant research questions. Perform statistical analysis on survey/experimental data