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, with a focus on handling non-determinism, model uncertainty, and changing data distributions in both pre-deployment and runtime contexts. Investigating runtime monitoring, testing, and explainability
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spatial distribution of recoverable resources. You will work in an interdisciplinary environment that brings together geophysics, mineralogy, and resource management, in collaboration with academic
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consists of five professors and several doctoral students and postdocs. We value our open, supporting and inspiring atmosphere with weekly activities throughout the academic year. You will have the
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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documented research experience (e.g. from postdoc or doctoral studies) from another university/institute or relevant experience from industry/public sector. As part of the application, the applicant shall
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ability. Additional requirements Very good oral and written proficiency in English. Significant documented research experience in the research area from doctoral studies and PostDoc from another university
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. There are multiple alternatives in the future deployment of nuclear, including centralized large-scale and distributed SMRs, and there is no established knowledge of how these would impact the power grid
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to teach a wide spectrum of entry level courses in English in, among others, programming, algorithmic problem solving, data structures, programming languages, and the analysis of algorithms. The applicant
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at the University of Gothenburg. To be eligible for appointment as a postdoc, the applicant is required to have a doctoral degree, a doctoral degree in art or a foreign degree that is deemed to be equivalent to a
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developing data processing pipelines, such as Nextflow or similar systems. Computer clusters and distributed systems. Virtual environments, e.g., Docker or Apptainer. Methods for software deployment (e.g