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and objectives Vision-language-action models (VLAs) Investigate VLA models and multimodal representations that connect visual observations and language instructions to robot behaviour, including
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entrepreneurship research, work with rich international datasets, and collaborate with leading scholars in the field. Responsibilities and qualifications Your primary responsibility will be to conduct high-quality
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that explain how humans learn, adapt and stabilise navigation behaviour in urban environments. The project will combine methods from transportation science, artificial intelligence, computational neuroscience
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of Aalborg University, preferably 3-6 months at a foreign research institution. Who we are The student will be anchored at the Center for Clinical Data Science (CLINDA) . CLINDA aims to conduct independent
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applications in underwater robotics and maritime technology. A central part of the project is the development of interpretable probabilistic models for changes in vehicle behaviour and operational regimes under
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-scale Microflora Danica dataset combined with samples taken from sites. You will conduct sampling, and apply experimental methods such as metagenomics and metatranscriptomics, linked to soil process data
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projects Actively participate in PhD courses Write scientific articles and finalize your PhD thesis Participate in international meetings Conduct a research stay at an institution abroad Teach and
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to develop and conduct a PhD project involving co-design and co-production in close collaboration with supervisors and research partners. Qualification requirements PhD stipends are allocated to individuals
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their behaviour upon addition to soil. Unlike mineral phosphate fertilisers, many BBF contain significant amounts of organic phosphorus (P) and calcium phosphate forms, which are not directly available to plants
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universities. The transition towards electrified and energy-efficient energy systems poses significant challenges in predicting the coupled behaviour of thermofluid, electromagnetic, and rotordynamic processes