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and predictions. Use cutting-edge methodologies such as A.I. and machine-learning to accelerate the numerical simulation processes. Develop optimal control strategies for energy systems Publish high
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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in at least one of the following: optimization, PDEs, optimal control, numerical analysis/scientific computing, or machine learning for differential equations and control. Evidence of research
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. Design and implement strategies for multiplexed detection, enabling simultaneous analysis of multiple analytes from small sample volume; optimize sensor design and assay for high throughput analysis
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. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code. Our research team at NYUAD (New York
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optimize algorithms for analyzing behavior data. Build or implement existing scripts to temporally align data across multiple modalities. Coordinate efforts with DNB researchers and established vendors (Med
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. The researcher will also design data-driven numerical approaches to address a variety of real-life optimization models including disaster and emergency logistics, supply chain and transportation. Specific
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matrices only model networks with one kind of objects or relations between the objects. Many real world networks have a multidimensional nature such as networks that contain multiple connections
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Informatics (Numerical Analysis; simulation, optimization and modelling tools; Computational Fluid Dynamics (CFD)), Product and Processes Engineering (Space Engineering), Condensed Matter Physics (Fluid
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development for 3D characterization of crystal shapes: - An existing thermal-optical-microscopy stage should be modified/improved allowing observation of the crystals along multiple axes, and thus the 3D