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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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and supportive Laboratory of Organic Electronics (https://www.liu.se/loe ). LOE currently has >150 researchers and research students across thirteen group sharing an open lab environment for fruitful
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods. Special
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creative and stimulating environment with the opportunity to network with both the business community and international contacts. Read more about our benefits and what it is like to work at SLU at https
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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Navigation and Perception Lab (https://rnp.aass.oru.se ) which belongs to the Centre for Applied Autonomous Sensor Systems (AASS) research environment (https://www.oru.se/english/research/research-environments
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transcripts, details of referees, letters of recommendation, etc.)” Welcome to apply! Where to apply Website https://lu.varbi.com/en/what:job/jobID:907263/type:job/where:39/apply:1 Requirements Additional
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the instructions in the job advertisement, and that it is submitted before the deadline. The instructions for applicants are available at: How to apply for a position . Where to apply Website https://su.varbi.com/en