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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 23 days ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
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, statistical analysis, and mathematical modelling. Provides support for use of developed methods. Remains up to date on the scientific literature of statistical methods and machine learning as applied
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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, Mechanical), Computer Science, Applied Math/Statistics, Physics—or related. Candidates who will graduate in the near future are also welcome to apply. Strong foundation in machine learning/deep learning and
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. Stefanos Zafeiriou can be found at https://wp.doc.ic.ac.uk/szafeiri/ . Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. computer vision, machine learning
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied