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on the modelling of transmission dynamics and risk assessment of mosquito-borne arboviruses with the potential to invade the EU. Addressing this challenge requires an interdisciplinary approach, combining
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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integrates spatio-temporal analyses (including synthetic descriptions such as distribution envelopes, size structures, and joint species distribution modeling), trophic modeling, and machine learning
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. To the best of our knowledge, there has been very little attempt to address this crucial problem in MT. This PhD proposes to investigate different methods to improve terminology translation in NMT models
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neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific experience. Our approach integrates
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neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific experience. Our approach integrates
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change initiatives. The Person You will bring: Expert knowledge of organisational design, operating models and large-scale people change A deep understanding of employment legislation and governance
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. These triggers can be injected into the training dataset or directly into the model weights. These are then called poisoned. Due to parameter-efficient fine-tuning methods, backdoor attacks on large language
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development – development of scenarios for the network analysis. --- Model development – development of an approach to investigate techniques to enable IoT devices with microwattlevel power consumption