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Field
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candidates using photometry, spectral energy distributions and, when available, spectroscopic data. Both fellows will produce diagnostic plots and summary tables. Simple statistical or machine-learning methods
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the NYUAD Kawader program, for details regarding the program, open dates, specific program requirements, and FAQ's please refer to our NYUAD Kawader webpage: https://nyuad.nyu.edu/en/about/careers
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methodologies. Additionally, CFD simulations combined with physics-informed machine learning will also be examined. Several research and industrial partners are a part of this project. The ideal candidate would
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welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We
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degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of image
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relationships and apply data-driven or machine learning approaches to guide molecular design and accelerate materials discovery Collaborate with internal and external stakeholders, including computational, data
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learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software development and debugging skills. Able
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English