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, implement and benchmark machine learning models for large-scale health datasets consisting of diverse information including structured medical history, demographics, clinical notes, laboratory measurements
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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of large structures. Additional information about CMEC facilities can be found online about the CMEC Equipment & International Code Council Accredited Testing Laboratory. Background Check: This position
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modelling and numerical relativity. The group comprises PI Alicia Sintes, a further ten faculty members and a large number of post-doctoral researchers and PhD students. The group is a member of the IAC3
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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approach. Further information about current and past research of the lab can be found on our webpage https://scan-psy.univie.ac.at/research/ Your future tasks: We are looking for a postdoc in environmental
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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practices and international standards. Leverage VIB Data Core services, including high-performance computing pipelines and large-scale GPU resources, to scale ML development and deployment. Your profile PhD