145 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Sweden
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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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be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice
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the time you start (you can apply before they are met e.g. during your PhD). A doctoral degree (PhD or equivalent) in an area relevant to the announcement. Everyone is welcome to apply but due to regulations
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application! Work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning
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clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
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practice or other forms of appointment/assignment relevant to the subject area. The successful candidate must hold a PhD in one of the following fields: mathematics, physics, computational science, or
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military service. What you will do The majority of your working time is devoted to your own research, in collaboration with other postdocs and PhD students, as well as collaborating groups. As a Postdoc you
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of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. For more information about the total announced post-doctoral positions within
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to develop solutions with real world relevance and impact. This project will be carried out in close collaboration with researchers from the Division of Material and Computational Mechanics at IMS and the
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman