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Field
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Sciences, Geodesy, Physics, Mathematics, Computer Sciences, Civil & Environmental Engineering, or a related field. PhD awarded no more than four years prior to the effective date of appointment with a
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collaborators. Requirements: Applicants should have a PhD in Computer Engineering, Computer Science, or a related field. Extensive and sound knowledge of ML, AI, DNN, LLMs/VLMs, Multimodal LLMs, RAG, Agentic
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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. The Postdoctoral Associate must hold an MD or MD/PhD degree with clinical research experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in
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. They must hold an MD or MD/PhD degree with clinical research experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in vascular
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simulations using open-source code LAMMPS Publish research results in peer-reviewed journals Present findings at scientific conferences Required Qualifications PhD in plasma physics or a closely related field
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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of the upgraded Swiss Light Source synchrotron, as well as other facilities around the world. Profile We are looking for a person with: PhD in physics, engineering, computer science, or a related field. Strong