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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 1 day ago
communication, sociable with an appetite for working in a group. Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning
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to simulations on quantum simulators and computers, and applications of machine learning, including neural quantum states. Candidates will benefit from state of the art computing resources at the Argonne
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observational data of non-CO2 greenhouse and ozone-depleting gases (e.g., N2 O, CFCs, HFCs), and to extract diagnostic insights from instrument performance indicators, using machine learning methods. Additional
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with conducting research at the intersection of reinforcement learning and statistics. The primary responsibility of the candidate will be to advance statistical methodology useful for the design and
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. We envisage combining process-based modelling and machine learning, as well as integration of Earth observation data into the modelling framework. The objective is to quantify the GHG budgets
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About the Opportunity Job Summary: The Lopez Group at Northeastern University is seeking a postdoctoral researcher with experience in machine learning and organic chemistry to join a collaborative
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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of 3D data / point clouds. Knowledge of libraries or tools such as PCL, Open3D, PDAL, and CloudCompare, as wellas machine learning/deep learning methods applied to 3D data, will be considered an asset
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with