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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing can be
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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Pneumatic Tires, Structure-Process-Properties Relationships. As part of it, we are currently looking for a postdoc on machine learning for road characterization. How will you contribute? Do you have proven
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information that is processed through a machine learning element. The role will also require regular contributions to a variety of academic tasks, including positively interacting and communicating with
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. We are now looking for: Three (3) Doctoral Researchers (PhD students) in Machine-Learning-Driven Atomistic Simulations The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro
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Details Title Postdoctoral Fellow in Geometric Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Applied Math Position Description A
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF