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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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/ ) at the Department of Clinical Microbiology, at Umeå University (https:// www.umu.se/en/department-of-clinical-microbiology/ ), the PhD candidate work in the Marie Skłodowska-Curie (MSCA) Doctoral Network GLYCOCALYX
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Lecturers are non-tenure track teaching faculty members who are hired on multi-year appointments with the expectation of renewal and promotion. We are seeking candidates who can teach large master’s level
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some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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» OtherEducation LevelPhD or equivalent Skills/Qualifications Who you are PhD in Computer Science, Machine Learning or related field or 5+ years of relevant industrial experience. Strong track record of developing
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to prepare project reports and co-author research papers. The ideal candidate is expected to be working towards a PhD in Computer Engineering, Computer Science, Computer Systems Engineering, Electronic
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focus on the dynamic nature of phase transitions in APIs, using machine learning interatomic potentials (MLIPs) to construct force fields whose mathematical complexity will be carefully controlled in
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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High