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. The successful candidate should apply state-of-the-art methods in either aquatic ecology and biodiversity research (e.g., environmental omics, eDNA, etc.) or hydrology (e.g. integrated modeling and/or AI-based
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well as experience with simulation model calibration, validation, and prediction. While efforts will be collaborative, the person in the role is expected to work independently and publish results through scientific
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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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significant challenges for the treatment and supply of safe and high-quality drinking water. The project aims to develop a multiscale predictive analytics framework that integrates long-term algal speciation
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. Oversees the delivery and operating model for UFA’s data management and reporting function–including roadmaps, Jira/ServiceNow workflows, release/change management–ensuring predictable, transparent, and
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modelling photonic devices and physical reservoir computing systems. The activities within the project will benefit from synergies with other projects in the group as well as with other activities
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University Work duties: This postdoctoral position is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on materials modelling of chemical stability in aqueous organic redox flow
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) for predictive modeling. Collaborate with neuroscientists, biostatisticians, and clinical researchers within BBRC and external partners. Contribute to manuscript preparation, presentations, and dissemination
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is to move beyond traditional “check-after” approaches and instead predict and prevent errors while the radiotherapy treatment is being delivered. The candidate will build upon this existing prototype
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and observation models to reflect real-time changes in environmental conditions, enabling more accurate predictions of adaptation impacts and thereby supporting a better-informed, resilient decision