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to multidisciplinary research aimed at advancing military medicine. What will I be doing? This opportunity offers a hands-on learning experience within a collaborative research environment focused on combat casualty
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supporting documentation, proven experience in all of the following areas: natural language processing and machine translation (sequence-to-sequence modelling, NMT, glosses); deep learning, Transformers, and
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on the design and development of mechanically intelligent surgical tools that simplify surgical motions and incorporate image sensing. These tools will be integrated with robotic platforms and machine learning
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an exciting place to learn and
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denoising, cell segmentation and the analysis of cellular neighbourhoods and cell–cell interactions. Experience in developing artificial intelligence and machine-learning methodologies for multimodal data
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technology or Artificial Intelligence (AI) tools to solve business or administrative problems, demonstrated knowledge with Machine Learning (ML) and AI tools. Strong integration experience in enterprise
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relevant background in information extraction, entity resolution/entity linking, machine learning, uncertainty modelling, explainable AI, or a closely related area, with a PhD (or near completion) in
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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, Bioconductor, tidyverse, SingleCellExperiment, or related software. Experience with Python and machine learning approaches is considered an asset. APPLICATION PROCEDURE Applicants should submit: Cover letter
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn