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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological data analysis and topological machine learning; AI-assisted theorem proving
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Plasmodium genomic data analysis; see https://mrc-ide.github.io/PGEforge/. Data partitioning: The 24,409 analysis-ready samples will be extracted and partitioned into subsets by country (since this is the most
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, microplastics; by means of numerical and experimental approaches including high-performance computing, data science methods via machine learning/AI and digital twins, enhancement, development and of application
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natural language processing with causal estimation. Recent directions in the project include using large language models to remove treatment-predictive information from text, benchmarking debiasing
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or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the
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machine learning approaches to biological data Strong communication skills and ability to collaborate in a team-oriented environment Be Bold. About the Position: Design, create, and enhance workflows
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20th September 2026 Languages English English English The Department of Materials Science and Engieering has a vacancy for a PhD Candidate in large language models(LLMs) for data extraction and
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low latency and high capacity optical switched AI computer network architecture empowered by the developed photonic integrated WDM switches and controls. The PhD candidates will contribute to the TU/e
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. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning, and works on