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Field
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Your Job: In an interdisciplinary team, you will implement approaches for the automated, large-scale availability and integration of energy system data and models, applying data science methods
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manipulation systems for contact-rich tasks Key research challenges include: Learning reliable predictive models from sparse and noisy sensory data Incorporating semantic priors into planning and control
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Latin intellectual traditions. The project combines intellectual history with computational text analysis to examine large, historically complex corpora spanning multiple centuries and languages. The core
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enterprise data into usable models (Snowflake preferred). Experience working with large-scale administrative systems such as Banner, Workday, Salesforce, or ServiceNow. Effective analytical thinking and
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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17 Apr 2026 Job Information Organisation/Company IMT Atlantique Department Doctoral division Research Field Computer science » Other Researcher Profile First Stage Researcher (R1) Positions PhD
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. In addition, you will develop and leverage training data sets derived from years of ARM metadata records, as well as design and implement Large Language Model (LLM)-based conversational interfaces
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25 Feb 2026 Job Information Organisation/Company Constructor University Bremen gGmbH Department School of Science Research Field Computer science Researcher Profile Recognised Researcher (R2
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, governance structures, and integration patterns, ensuring that data is trustworthy, discoverable, interoperable, secure, and prepared for advanced use cases such as RAG, vector search, semantic layers, and
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales