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Project and Research Environment Large Language Models (LLMs) are increasingly used in legal question-answer contexts. It is now well known that answers may contain noise, for example, in the form of so
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analysis, while other competence areas include e.g. causal inference, latent variable modelling and prediction models. The section aims to maintain a strong position in these areas but also wants to expand
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on the candidate’s profile and interests, the position may also involve primary data collection, including extraction of medical chart data using natural language models and surveys of stakeholders within child and
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electromagnetic simulation tools, such as CST Studio Suite or Ansys HFSS, and you use them systematically as part of your design workflow. You also have practical skills in numerical modelling and data analysis
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sensory testing (QST) as well as research based on animal models (e.g. rodents and pigs). CNAP is a dynamic and international research environment: approximately 60% of our staff is international, with a
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-4DµXRD results. Implementing crystal plasticity finite element models that use the experimental data as input and to understand the observed local strain heterogeneities You will work in close
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metagenomics from various field experiments, evaluation of microbially mediated regulation of soil carbon fractions, and development of a model that simulates the growth of key soil microbial communities related
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founders develop social business models by working systematically with societal problems, solutions, impact logic, value propositions, funding models, stakeholder engagement, and implementation pathways
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genetic engineering of the selected microorganism to increase its yield and robustness, followed by large-scale cultivation in a 100 L bioreactor. The post doc position is embedded in a larger
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grant project “Driving innovation in crop resilience through Comparative QTLomics.” The selected candidate will contribute to five main objectives: 1. Apply large language models (LLMs) to collect