15 algorithm-development-"Multiple" "NTNU Norwegian University of Science and Technology" PhD positions at Linköping University
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multiple subfields represented, including animal behaviour, evolution, ecology, genetics, zoology, conservation, microbiology and animal welfare. See: https://liu.se/en/organisation/liu/ifm/biolo
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research, undergraduate and postgraduate education within the field of biology, with multiple subfields represented, including animal behaviour, evolution, ecology, genetics, zoology, conservation
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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prominent approach to AI, with impressive performance in many application domains, including materials discovery. This development has a huge potential for societal impact, with applications in renewable
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of FADOS is to achieve targeted modification of semiconductor properties through electronic doping to control and modify their electronic characteristics. The project’s goal is to develop fundamental
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electronic doping to control and modify their electronic characteristics. The project’s goal is to develop fundamental understanding and innovative fabrication processes to solve urgent problems in organic
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data sharing platform developed in earlier projects, the student will develop interfaces for interacting with the ontologies, and related datasets, in a user-focused manner. This will involve both
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trustworthy, we facilitate large-scale and reliable use of AI across different industries. Your work assignments You will work at the intersection of machine learning, cybersecurity, and privacy, developing
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reliable use of AI across different industries. Your work assignments You will work at the intersection of machine learning, cybersecurity, and privacy, developing methods to make AI systems trustworthy