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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting- edge deep generative probabilistic models
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In manufacturing, a wide variety of use cases exist where Deep Learning (DL) and Machine Learning (ML) are successfully applied. Examples of use cases include the production of rockets, stem cells
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
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• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.) • Literature review/summarizing skills
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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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project - “Safe-assured learning-based deep SE(3) motion joint planning and control for unmanned aerial vehicles”. He/she will be required to: (a) assist in literature review, data collection, algorithm
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Intelligence, Machine Learning, or relevant fields. Strong theoretical research capability, particularly in the theoretical analysis of optimization, convergence, stability, and/or generalization of deep
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Learning and software engineering with at least 3-5 years of research experience (including PhD training) in related fields. Proven track record in research and development of cybersecurity and/or Deep
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Performing retrofitting, post-training, and evaluation of SOTA open-weight models Publishing results in top-tier conferences and journals Implementing research ideas using modern deep learning frameworks
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 12 hours ago
; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and