154 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Sweden
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- Chalmers University of Technology
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, you must hold a PhD (awarded no more than three years prior to the application deadline*) in computer science, maritime transportation, or a related field, with a strong foundation in mathematics and
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(PhD students, post-docs, researchers and teachers) and is located at the BMC-building (Biomedical Center). The expertise includes computational and experimental mechanics of biological tissues, where a
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on machine learning solutions and data visualisation. In addition will some cod individuals be tagged, and their behaviour be monitored using acoustic telemetry. The cod behaviour could also be correlated with
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-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
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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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bioinformatics, physics, statistics, computer science, computational biology, or related fields. Experience programming in Python (or R) as well as bash/shell scripting. Experience with machine-learning and deep
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Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may
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investigation. Being part of a larger collaborative project, the postdoctoral researcher will be involved into discussions within a broad range of fields including computational, medicinal and organic chemistry
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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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-assembly mechanisms, identifying robust experimental signatures of collective properties, exploring practical applications, and utilizing artificial intelligence and machine learning to aid in this process