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to develop engineering interventions to improve plant regeneration, seed longevity and defenses against disease. Your task will be to further develop two-dimensional and three-dimensional models of plant
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algorithms. model and optimize end-to-end physical-layer performance, hardware non-idealities, and overall power consumption. focus primarily on space and security topics (satellite communications, reliable
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots
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of both the basics and the latest developments of machine learning, in particular large language models and other generative AI modelsOperational Language Requirements: Fluent written and verbal
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agricultural sector in which agriculture goes hand in hand with soil health and nature restoration, and a good revenue model for farmers. Between now and 2030, ReGeNL will start the transition to regenerative
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theory and non-standard logics, preferably within one of the two branches of the project: Modal Logic of Forcing Inner Models from Extended Logics These are active fields within set theory, with many
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building models (impervious and with openings) capturing loads and pressures on various structural elements, both horizontally and vertically. Integrating experiments with numerical simulations (e.g. CFD
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of a locally calibrated fire spread model that accounts for land management and design scenarios (PhD 1) - the generation of near-real-time, high-resolution maps of fuel loads, fuel moisture
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 2 months ago
to combine high-resolution multi-omics profiling, advanced computational modeling, and clinically aligned biomarker stratification, in order to generate actionable, patient-specific predictions and resistance
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will be broad, but primarily include: Analyzing large data sets for input into assessments and biological advice Follow the development and support the transition to next-generation assessment models