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new flagship research program aiming to to map the molecular structure and function of single human cells in time and space and create AI-based models to predict human cells. It is funded by the Knut
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includes the ecological knowledge required to predict and control future insect damage in boreal and temperate forests. Duties The employee is responsible for developing research in the subject area forest
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: Experience in tree breeding, knowledge of selection algorithms/predictive models, programming skills, experience in breeding simulation, first authorship, conference presentations, strong problem-solving
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prediction tools, such as AlphaFold2 or Rosetta, including multimeric complex prediction. You have experience of microbiome sequencing, genome mining, or metagenomic data analysis. You have worked with host
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. David Marlevi, Prof. Ulf Hedin, and Dr. Ljubica Matic to improve stroke risk prediction for patients with carotid atherosclerosis using a multidisciplinary combination of data-driven imaging
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mathematical models, validating them with experimental data, and making predictions. The ultimate goal is to decode the mechanisms behind intra-organelle coordination. Besides plant cells, such coordination is a
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, mitochondria, and chloroplasts. The project will involve developing mathematical models, validating them with experimental data, and making predictions. The ultimate goal is to decode the mechanisms behind intra
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that enhance the quality and efficiency of forest management planning. The PhD student will combine remote sensing with machine learning to detect cultural remains, predict terrain accessibility, identify
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responsibility is to conduct high-quality research on hybrid artificial intelligence. You will: Combine deep learning to capture long-term patterns and uncertainties with stochastic model predictive control
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming