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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
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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results. Preferred Qualifications: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud
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in the laboratory QUALIFICATIONS Successful applicants will have: PhD with expertise in cancer biology, proteomics and/or computational biology / machine learning A proven track record with first
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experience Evidence of experience with public-patient involvement and stakeholder engagement Track record in scientific publication and dissemination Experience with Machine Learning Algorithms and Artificial
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): Research experience with literature review, data collection from different stakeholder groups. Advanced skills in using Stata or R. Machine Learning experience Artificial Intelligence Excellent written, oral
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to these values ensures that we foster a culture of mutual respect, open collaboration, continuous learning, and innovative thinking. Join us at RCSI, where your contributions will be recognised, and you will be
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Simulation group to apply classical Molecular Dynamics and Machine Learning approaches for development of a new class of hybrid polyphenol-lipid nanoparticles with tuneable internal structure and exploration
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory