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Field
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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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of these reusable packaging using IoT sensors and deep learning techniques embedded in the sensors. During the preliminary work, neural network models were developed to perform simple tasks using accelerometer data
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
., Data-driven Flower Petal Modeling with Botany Priors, CVPR 2014. 2. Q. Zheng et al., 4D Reconstruction of Blooming Flowers, CGF 2017. 3. S. Ghrer et al., Learning to Infer Parameterized Representations
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experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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, or similar, and a degree in Environmental Engineering, Environmental Science, or a related quantitative field. Position 2 will focus on large-scale data analytics and machine learning. Applicants should have