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for We are seeking a Research Associate with the following characteristics: Essential: PhD or equivalent in statistics, biostatistics, applied mathematics, computational biology, epidemiological modelling
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. Responsibilities include querying databases, data processing, supervised and unsupervised machine learning, deploying production models, and communication of scientific findings via peer-reviewed publications and
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-house AI projects, ensuring alignment with business goals and technical feasibility. Design and create high-quality prompts for AI models, ensuring they are clear, concise, and aligned with the desired
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alignment to create system-level composite images. Build and train advanced machine learning models to autonomously detect various PV defects such as hotspots, microcracks. Execute the necessary data
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Description for the project “Functional investigation of Salmonella -microbiota-host interactions by using novel microaerobic intestinal organoid models from humans and chickens (SalMIOM
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(DFT), machine learning force fields (ML-FF), and data-driven machine learning models to investigate reaction mechanisms, catalyst activity, kinetics, and structure–function relationships
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of advanced technologies. This includes developing cutting-edge AI/ML models and datasets; learning how to take advantage of unparalleled computing resources in the academic environment by optimizing AI/ML
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The post of Research Associate in Large Language Models for Process Automation will contribute to the development of an AI-powered framework for risk assessment and safety in continuous chemical
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modeling in the Department of Physical Oceanography for three years (until 31.12.2028) and a percentage of 75% (30h/week), subject to the funding of the project. Remuneration is paid in accordance with
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techniques (especially program synthesis) to improve the explainability and robustness of AI models Participating in co-operation with Continental’s development team Interacting with Continental business areas