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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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take responsibility for the planning, execution and analysis of high-quality research, ensuring the validity and reliability of data at all times and will maintain ongoing scientific discussion with
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-matter interactions, development of automated AI/ML systems for novel materials processing and quality insurance. The lab combines additive manufacturing, large scale laser micro-processing, multiscale
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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured light-trapping
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optimization, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured
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demonstrated independent thinking and problem-solving abilities. Experience using large databases is preferred, and genetic data analysis skills are desirable. Required Qualifications PhD or MD in Public Health
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a relevant subject (epidemiology, biostatistics or big data) and have experience of working with Swedish population registers on epidemiological studies. You will possess sufficient specialist
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Fixed-term: The funds for this post are available for 36 months in the first instance. We are looking for a Post-Doctoral Research Associate (PDRA), with a PhD in Physics, Materials Science or
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. Required Qualifications: PhD in statistics, economics, computer science, operations research, or related data science fields Strong data science skills, including experience working with large, complex data