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position is embedded in a vibrant research environment that includes several PhD students and postdoctoral researchers. The project is a close collaboration between the Computer Vision Group at Chalmers
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postdoctoral position in data analysis, where you will apply machine learning techniques to understand how resistance genes spread and to help detect infections caused by resistant bacteria. The position is part
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description The postdoctoral project is focused on development and the exploitation of machine learning tools to accelerate the analysis of microtomography data at the MAXIV synchrotron facility. MAXIV
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
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the postdoctoral appointment’s nature as a career-development position for junior researchers, we are looking for candidates who have completed their PhD no more than three years before the application deadline
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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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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include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment
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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