25 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions at Uppsala universitet in Sweden
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
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on developing biochar as a sustainable feedstock for hard carbon anodes in sodium-ion batteries. In collaboration with Besca AB (https://www.bescacarbon.com/ ), this project explores microwave plasma technology
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Website https://uu.varbi.com/en/what:job/jobID:911571/type:job/where:39/apply:1 Requirements Research FieldTechnologyEducation LevelPhD or equivalent Research FieldTechnologyYears of Research Experience4
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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knowledge. The required training for teachers in higher education may be completed during the first two years of employment if there are exceptional grounds. documented ability to teach in Swedish or English
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https://uu.varbi.com/en/what:job/jobID:907722/type:job/where:39/apply:1 Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD
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to apply Website https://uu.varbi.com/en/what:job/jobID:907491/type:job/where:39/apply:1 Requirements Research FieldChemistryEducation LevelMaster Degree or equivalent Research FieldChemistryYears
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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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Python) and data analysis or machine learning applied to materials science Ability to work in interdisciplinary project or industrial experience About the employment The employment is a temporary position
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational