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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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reference architecture for data visiting. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine
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2 Oct 2025 Job Information Organisation/Company Tallinn University of Technology Research Field Computer science » Programming Computer science » Other Engineering » Materials engineering
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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
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analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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methods in forestry generate vast quantities of data and demand more accurate information. Machine learning allows for the systematization and processing of this data into new forms of information
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partners. Applicants should fulfil the following requirements: A master’s degree in engineering or science, with a focus on computer/data systems, energy technology, software/hardware, information technology
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will