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Universiteit Amsterdam welcomes applications for a two-year Postdoctoral position in Reinforcement Learning for Stochastic Optimization. The candidate is expected to conduct high-quality research
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. Responsibilities include: Designing and executing behavioral learning paradigms with social and non-social reinforcement In vivo calcium imaging during learning and sleep Quantitative analysis of neural activity
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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-changing technologies. Life-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a Postdoctoral
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datasets [e.g. behaviour, simultaneous EEG-fMRI and eye-tracking data]. Main research themes include, but not limited to: reinforcement learning and valuation, risk and uncertainty, confidence and
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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their effectiveness remains limited by the inherent constraints of fuzzing techniques. As an alternative, we propose exploring reinforcement learning (RL) as a promising approach for vulnerability assessment in SoCs
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, or similar ML frameworks Experience with large-scale training or inference of LLMs Interest in LLM alignment, reinforcement learning, or generative AI systems Fluency in English; clear communication, problem
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, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models and their development Strong analytical