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Field
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Abrahao (NYU Shanghai) and João Sedoc (NYU Stern). Research Focus Areas Our research encompasses topics in DL and AI, including but not limited to: Deep Learning Algorithms and Paradigms Generative Models
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algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and neuroscience. The candidate is
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balance simulations that support estimates of snow distribution for biodiversity and ecosystem assessments, as well as hydrological modelling and management plans for ski resorts and hydropower companies
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experience. Experience in analysing large, complex ecological or biodiversity datasets. Strong proficiency in statistical modelling, including experience with species distribution models, community ecology
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related to investigations into wildlife disease dynamics, assessment of wildlife abundance and distribution, and environmental parameters. Preferred Qualifications Prior experience with coordination of key
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. 1. Merit-Based Evaluation The Selection Committee will verify and assess the merits documented by the applicants. The maximum score for this phase is 80 points, distributed across the following
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
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complexity, the impact of blowing snow on local and regional scales. The PhD candidate will produce mass balance simulations that support estimates of snow distribution for biodiversity and ecosystem
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins