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prediction models in Neurology using EEG data via Deep Learning (DL) techniques. In this prospective and longitudinal study, the outcome of interest is cognition over time. This position will be under
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infrastructure recommendations. As part of this work, we are seeking a Research Fellow to lead the development of a user-friendly GUI tool that will enable stakeholders to interact with planning models and
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Responsibilities: Explore the frontiers of intelligent sensing by advancing cutting-edge theories and pioneering transformative technologies. Integrate wireless sensing with Large Language Models (LLMs) to pioneer
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to gender—and demonstrated expertise in advanced statistical methods (e.g., multilevel modelling). The successful candidate will work closely with the Principal Investigator, Dr. Verena Klein, a PhD student
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, internationally recognized for its high-impact research, investigates new techniques and technologies for engineering advanced biomaterials and complex 3-dimensional (3D) tissue models applied to musculoskeletal
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-related illnesses (HRIs) in Singapore’s context. This study aims to establish baseline HRI data and integrate this with climate projection models to assess future health burdens and intervention strategies
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signal processing models to analyze waveforms generated by traditional patient monitors and a novel wearable sensor and build a decision support tool to guide CPR Participate in the user interface and
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collaborative team advancing concussion care through innovation Research Fellow role driving consumer-led models in brain injury care The Opportunity Join a dynamic research team at the Monash-Epworth
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models for children. College / Directorate College of Engineering, Design & Physical Sciences Department Department of Mechanical and Aerospace Engineering Full Time / Part Time Full Time Position Title
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examining modulation of the tumor microenvironment and obesity-related colorectal cancer (CRC) disease progression using tissue-engineered patient derived xenograft (PDX) CRC models validated through