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cooperation with the Institute of Marine Research. The core of the position will be on development of new deep learning methods for segmentation/classification of data with limited and weak labels. Your may
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methods will be advantageous: Microbiological techniques, as well as computer-based analysis Molecular biology techniques Analysis of high-throughput datasets (DNA seq, microbiological data, competitive
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micrometeorological and remotely sensed datasets, as well as the analysis of soil and plant samples. Advance experimental design techniques by developing, evaluating, and refining innovative measurement methods
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approaches to fabrication prediction, structural and material optimisation, computer vision for process monitoring and quality assessment, and the integration of machine learning methods within design
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, stable homotopy theory, computer algebra. All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based
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, or similar systems) Experience or strong interest in: Benchmarking and evaluation of complex AI systemsm Robustness, uncertainty estimation, and failure-mode analysis, Deployment of AI systems in high-stakes
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, organizations, and societies. The group’s research has a socio-technical basis and is predominantly based on qualitative research methods. The fellowship period is three years. Depending on the candidate and the
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humanities studies of sustainability, Norway, China and food systems. Applicants must have experience with mapping and programming methods in architecture and urban design, particularly in site analysis
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are defined and incumbent uses existing practices as guidelines to determine specific work methods and carries out work activities independently; supervisor is available to resolve problems. Involves general
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upcoming SPHEREx data. The candidate will perform all levels of data analysis, from the processing of raw data to maps to power spectrum estimation of resulting CIB maps. The candidate will also have the