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from the chromosome-level reference genome alignments. Historical and population comparisons will also be undertaken. This PhD position will be associated with the Ecology and Animal Science, Production
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resilience and sustainable forest stewardship in the face of global changes.Support includes a competitive funding package, and also includes: designated lab/office space, computer and software, conference
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 1 day ago
of Contract Temporary Job Status Part-time Offer Starting Date 15 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 54/08/25 Is the Job
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a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a holistic multi-hazard risk
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Scientific writing and communication You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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humans. The successful candidate will join the new Intelligent Robotics group in the Computer Science Department in the Computational Foundry in the Faculty of Science and Engineering at Swansea University
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such as CAPeX. You are an experimentalist - second to none. You have experience in one or more of the following areas: Analytical electrochemistry Vacuum science/surface science MEMS chip design and
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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analysis, and high-performance simulations? We offer a challenging PhD position in the Mathematics of Computational Science group to design and analyse novel structure-preserving methods for stochastic