18 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" PhD positions at Uppsala universitet
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June 2026, UFV-PA 2026/581 Where to apply Website https://uu.varbi.com/en/what:job/jobID:908266/type:job/where:39/apply:1 Requirements Research FieldMedical sciencesEducation LevelPhD or equivalent
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) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks
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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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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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methods, including GIS and spatial analysis. Applicants are invited to visit our departmental website to review the kinds of research we do in the department (https://www.uu.se/en/department/human-geography
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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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. 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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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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access to preventive care and neighborhood characteristics influence long-term health trajectories. The project applies both econometric and machine learning approaches to identify high-risk groups and to