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collaborate closely with the project team, including co-supervising the project's PhD student and working with partners at the University of Texas at Austin. In terms of study area, you will primarily focus
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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spectral based sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform
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, analysing results and writing scientific articles. To qualify for this role, you need to have the following qualifications/experience: Have a PhD in chemistry, physics, engineering or a closely related
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, conducting experiments, analysing results and writing scientific articles. To qualify for this role, you need to have the following qualifications/experiences: Have a PhD in chemistry, physics, or a closely
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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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for the sustainable planning of wind power in forest landscapes. For more information, see: www.slu.se/forskning/forskningskatalog/projekt/w/windyforests/ Your profile You must hold a PhD in forestry, ecology
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, ROS) Solid skills in numerical analysis Advanced knowledge of computer vision Experience in human–robot interaction Particularly Meritorious It is particularly meritorious if the applicant has: A PhD
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware