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computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability to communicate scientific results clearly through
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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, machine learning, and optimization, broadly defined. Applicants working at the intersection of these areas, especially those applying theoretical and computational methods to problems in management science
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are seeking a highly motivated Postdoctoral
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design and discovery, including the use of artificial intelligence (AI) and machine learning (ML) techniques. The hired candidate will focus on computational aspects of immune repertoire analyses
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next generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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Physics Teaching Postdoctoral Fellow The Department of Physics and Laboratory of Atomic and Solid State Physics at Cornell University invite applications for a two-year postdoctoral fellowship
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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python