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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable
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applications for faculty positions in Computer Science. Faculty specialising in data science, machine learning (deep learning, reinforcement learning, multimodal learning), Generative AI, and computer graphics
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Computer Engineering. Expertise in computer vision algorithms and image processing techniques (such as object detection, segmentation, and feature extraction). Proficiency in deep learning frameworks such as
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Design and use of data spaces and digital twins for materials and autonomous material laboratories Use of deep learning methods to connect theory, simulation, and experiments Integration of high throughput
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the academic department in the Faculty of Arts and Sciences relevant to the incumbent’s own field. The Lecturer will teach a number of graduate-level research methods and data science courses and will be
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks to recruit a Junior Research Scientist to work on Deep Reinforcement Learning (DRL