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languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning, or another form of data-intensive academic research. Have a strong interest in interdisciplinary
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framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
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practical applications, including solving mathematical reasoning problems. The ideal candidate has a strong background in machine learning and an interest in bridging rigorous theoretical insights with
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models' answers. If necessary, we will then propose dedicated learning strategies for inducing
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
, biology and medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we are modeling every nuance of how humans and animals look and move. We develop
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expertise in programming (C++, Python), computer architectures and Deep Learning (PyTorch, TensorFlow). Exceptional international candidates may be eligible for a fee waiver (read the following section
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Water Fluxes. Your tasks Build hybrid models, process-based and deep learning models, to capture ecosystem flux dynamics across space and time Develop generalizable models robust to climate variability
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Science, Machine Learning, Finance, FinTech, Economics, or a related field. Candidates should demonstrate knowledge of Large Language Models, generative AI, and machine learning, with interest in financial