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PhD position in Experimental Physics with focus on photonics and materials science (applied aspects)
Network SPARK for the doctoral project ‘Spatiotemporal Information Processing’. Last day to apply is November 30, 2026. The earliest starting date is February 2027, or by agreement. Project description
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brief. Copies of student theses and publications (if any). Names and contact information of at least one reference person. You apply via our e-recruitment system Varbi. The deadline for applications is
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information about the terms and conditions of employment during this period, please see What we offer . During the fourth year, the doctoral student will be employed by and based at Umeå University, Sweden
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of microbiome and clinical data; experimental studies of microbial adhesion and biofilm formation; analysis of carbohydrates and proteins involved in host–microbe interactions; and protein profiling
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. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming
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of marine ecosystem monitoring across spatial and temporal scales. The project will include work with remote sensing, autonomous sensors, environmental monitoring technologies, data integration, and system
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Mars, bridging laboratory spectroscopy with data from ongoing and upcoming NASA and ESA missions. The position is for four years of doctoral studies, including participation in research and postgraduate
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theses on advance level or other publications, contact information for at least two reference persons. Further information Further information is provided by Michael Holmboe, Associate Professor, e-mail
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on advance level or other publications contact information for at least two reference persons. Further information Further information is provided by Michael Holmboe, Associate Professor, e-mail
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architecture and viral factories within infected cells. The project will combine virology and structural biology approaches and involves training in cellular cryogenic electron tomography (cryo-ET) data