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Austrian Academy of Sciences, The Institute for Habsburg and Balkan Studies (IHB) | Austria | 13 days ago
guidelines, co-supervising two student assistants Conduct in-depth historical and philological analysis of the Frangochiotika sources jointly with the PI Lead the project's micro-level analysis of vernacular
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laboratory experimentation with advanced characterization techniques and data analysis to study water-rock interactions, crystallization processes and metal immobilization. Main responsibilities include
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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sensing, machine learning, historical map analysis, records-based research and selective archaeological surveys. Records of these sites are being made available in an open source Arches (https
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https://equity.unl.edu/notice-nondiscrimination/ . Minimum Required Qualifications Ph.D. in human dimensions of natural resources/agriculture, sociology, or a closely related field. Experience in
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Statistics and/or postgraduate studies related to data modeling will be valued -Knowledge of epidemiological analysis software such as R and QGIS -English Language -Previous experience in data analysis and
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Associate to join the Parallel Systems Lab (https://psl-ntu.github.io ) at NTU’s CCDS. The role focuses on designing, developing, and evaluating efficient, scalable systems for modern multi-agent HPC-AI
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; Application of molecular biology, biochemistry and microscopy techniques for the characterization of samples; Analysis and interpretation of experimental results and contribution to the preparation
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and adult zebrafish using microinjection, infectious disease challenges, analysis of immune-relevant gene expression, in vivo imaging, isolation of innate immune cells for in vitro analyses, and
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: 1. Take a leading role in the design and conduct of the process evaluation. 2. Take a leading role in the qualitative interviews and analysis of qualitative and mixed methods data. 3. Undertake