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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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an optimal molecular representation (including data procurement) and integrating generative model and binding oracles. Propose an algorithm to bias the generative models towards desirable properties, such as
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, generative design, building performance optimization, digital design methods (e.g., predictive modeling, multi-agent systems and algorithmic techniques for architectural design), digital design epistemologies
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transformative advancements in scientific discovery. Responsibilities As a Senior postdoc, you will be able to contribute to designing your own project focusing on implementing cutting-edge algorithms for graph
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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. The project aims at three significant academic contributions: (1) to explore and map out distinctive moral reasons to be concerned with extreme wealth from the perspective of distributive justice, (2
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with special emphasis on impacts of climate and environmental factors on fish early life history, growth, reproduction, survival and distribution including invasive species. You will closely work with
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separate job advert “Postdoc in advanced reconstruction algorithms for time-resolved nano CT of sustainable cement ”) Research and teaching efforts at a section and departmental level as appropriate and
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field