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work well within multidisciplinary teams. A strong publication track record in high-quality international journals or industry experience in using Shima Seiki WholeGarment machine would be an asset
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nowcasting platform that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes
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solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we
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to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies economic assessments machine learning or proxy-model based methods field
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resulting precipitation and extreme weather. We study global and regional climate change and are at the core of international community climate modeling efforts that also involve AI and Machine Learning. We
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional
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, engineering or computer sciences) and who have not worked in the life sciences before. 7 May 2025: Deadline to initiate a Letter of Intent 15 May 2025: Deadline to submit a Letter of Intent 25 September 2025
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studying for a doctoral degree in the arts, science, law, commerce or agriculture. Availability Available to New Zealand Citizen or Permanent Resident Students Our website uses tracking technologies to learn
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postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets