315 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Edinburgh Napier University" research jobs at Nanyang Technological University in Singapore
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the willingness to learn and grow Inquirer, critical thinker Ability to work independently and collaboratively Organized, reliable, patient, and with strong initiative Responsibilities: Conduct data collection and
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Electronic Engineering at Nanyang Technological University. The group focuses on the development of hybrid integrated photonic chips and experimental platforms for advanced sensing and information processing
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research grants in the above areas Job Requirements: A PhD degree in Computer Science, Data Science, Engineering, or a related field. Research experience in Computer Vision, Image Processing, Multimedia
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for machine learning Competent in soft skills / general personality traits e.g. information visualization, interaction design We regret to inform you that only shortlisted candidates will be notified. Hiring
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have the ability to be excited about working in a diverse lab group setting, plan and conduct assessments independently and collaboratively, interpret data, and present results to diverse audiences
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from chip. Characterise and optimise the algorithm for signal data processing for real-time cell analysis. Develop a user-interface platform for end-user testing of clinical samples. Job Requirements
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an emphasis on technology, data science and the humanities. Our team’s mission is to understand how the brain works in both normal and diseased states. We are particularly interested in neurodevelopment, what
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety testing Presenting
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spatiotemporal systems by combining physics-driven baselines with data-driven correctors. Formulate and solve inverse problems using Physics-Informed Neural Networks and relevant methodologies. Conduct rigorous