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Project Overview We are hiring highly motivated and talented Postdoctoral Associates who are interested in advancing the state of the art in resource-efficient machine learning at the Singapore-MIT
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technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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programming, large-scale optimization, and simulation-based optimization; and/or Expertise 2: Experience with general methods in artificial intelligence and machine learning, including reinforcement learning
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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and acquire spectral data Perform Raman spectral data analysis using machine learning algorithms Work with DiSTAP plant biology collaborators, training researchers in spectrosocpy Work with local
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Computer Science, Artificial Intelligence, Data Science, Automation, Electronic Engineering, Economics, Human Resources Management, or a related field. Experience with general methods in machine learning, such as
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of teaching and learning. Successful candidate will bridge the fields of AI development and education research, working closely with other AI researchers in M3S to contribute in both the creation of generative
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Computer Science, on topics related to Artificial Intelligence and Machine Learning, Human-Robot Interaction and 3D Vision/Multimedia. Experience with deep neural models and algorithms for 3D spatial reasoning and
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imaging, signal processing, or machine learning. Hands-on experience with ultrasound systems, particularly Verasonics platform is preferred. Familiar with programming languages and environments such as