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                Field
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                . Proficiency in programming languages like C and Python, as well as deep learning frameworks such as PyTorch and TensorFlow. Knowledge in imaging and computing device and equipment. Strong communication 
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                ) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena 
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                , cybersecurity, computer networking, machine learning, AI, ERP and cloud computing Conduct research with colleagues. Strong data analytics skills, including the ability to work with large sets of structured and 
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                A confluence of technological milestones in detector, computation and machine learning development, both globally and within the NRC, makes high-speed non-linear wavefront sensing for Astronomy 
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                researcher to help us deliver it. By combining coherent Raman scattering with machine-learning models trained on plant mutants, the project will shed new light on the cellular-level biochemistry that governs 
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                Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project 
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                tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other 
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                years of post-qualification experience at the time of application; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English 
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                experimental data from both literature and in-house experiment results Use state-of-the-art machine learning models to develop a multi-scale droplets evaporation model Assists in co-supervision of Final Year 
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                , including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems