872 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions in Sweden
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Field
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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for applicants are available at: How to apply for a position . Where to apply Website https://su.varbi.com/en/what:job/jobID:907631/type:job/where:39/apply:1 Requirements Research FieldPhysicsEducation LevelPhD
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regular project meetings and collaborate closely with other members of the research group. Publish scientific articles, both independently and in collaboration with others. Teach up to 20% of your working
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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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the world. More information about the Department can be found here: http://www.pcr.uu.se . We are seeking an internationally recognized scholar who is interested to contribute to further developing
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. Experience in analytical techniques such as Raman, MALDI-TOF-MS, HPLC is welcomed Willingness to learn new techniques (e.g., MicroCT Scan, FIB-SEM). Specific Requirements The degree must have been completed
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advice and by building a social network in their new hometown. http://www.sdcn.se . Project description The position will be associated with a project on topological phases in moiré materials, focusing
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or relevant topics be result-oriented and have high level of motivation and will to face challenges and conduct systematic research to solve them, if necessary, by learning/adopting new techniques/theories
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international academic institutions and 14 industry partners (https://euraxess.ec.europa.eu/jobs/401249 ). We work together in the field of fluid-structure interaction in technical systems and industrial