140 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Sweden
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-assembly mechanisms, identifying robust experimental signatures of collective properties, exploring practical applications, and utilizing artificial intelligence and machine learning to aid in this process
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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and will encompass major efforts at Sweden’s foremost universities over the course of 10 years. The vision is a sustainable future through materials science. Read more: https://wise-materials.org All
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for the position are: It is meritorius if you have solid experience with the following computer programs: CryoSPARC, Phenix, Coot, Chimera and Pymol, It is meritorius if you have experience working with eukaryotic
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, bacteriophages. Prof. Hauryliuk obtained his PhD in 2008 at Uppsala University, Sweden. His scientific contributions were recognized though the Ragnar Söderberg fellowship in Medicine (2014), the Swedish Fernström
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and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The tasks include primarily leading and conducting research
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for an excellent young life science or computational researcher to become Group Leader. Fellowships are targeted towards applicants to start their first independent group within a few years of their PhD. We offer
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for an excellent young life science or computational researcher to become Group Leader. Fellowships are targeted towards applicants to start their first independent group within a few years of their PhD. We offer
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and will encompass major efforts at Sweden’s foremost universities over the course of 10 years. The vision is a sustainable future through materials science. Read more: https://wise-materials.org All
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description