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Field
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particular emphasis on integrating satellite LiDAR and UAV data with field observations. Applying statistical modelling, automated machine learning approaches, and artificial intelligence for the analysis and
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We are recruiting full-time Research Fellows to develop hybrid physics-AI methods for weather applications Available data include: • Numerical weather prediction (NWP) model outputs
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate
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methods in meteorology (atmospheric data assimilation). This contributes to the cutting-edge research expertise of the College and the University in the strategic areas of Machine Learning, Statistical Data
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machine-learning, predictive-modelling or multivariate methods to behavioural and/or EEG data. Proficiency in R, MATLAB and Python. What we can offer In return we offer a generous pension, relocation
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demonstrated expertise in AI and machine learning, including experience building and evaluating models in frameworks such as PyTorch, and running workflows in HPC environments experience applying machine
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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preferred skill, including GIS, spatial suitability modeling, multi-criteria decision methods, and machine-learning-based feature importance. Exposure to resource questions relevant to large-load development
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning, Assessing