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Project descriptionAutonomous systems are intelligent agents—such as robots, vehicles, or drones—that can sense their environment, make decisions, and act independently. When multiple such agents
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Agentic AI is an emerging technology that fundamentally changes product development and evolution. We seek to study the implications of Agentic AI with the intent of accelerating the adoption
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Effective use of AI and AI agents requires high quality data. This research is concerned with studying what data companies should collect, how they should process it, where to store it and how
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topographic indices as environmental variables for mapping, and satellite data for weather. The doctoral student will be part of a broad research group with expertise in GIS, AI, soil science, forest ecology
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will be expanded with the recruitment of 19 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School
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silviculture or ecological surveys, ability to work independently in the field, skills in GIS and R-statistical analysis, and good communications skills in written English. A drivers license is a necessity for
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, the random dropout of participating agents, and uneven distribution of data across agents. As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
professors and a dynamic team of PhD students and postdocs working in areas such as wireless and optical communication, information security, information theory, and machine learning. At the department, we
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senior researchers, three postdocs and three PhD students. It is embedded in an interdisciplinary environment where we have close collaboration with other research teams at Chalmers such as technology
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on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual structures, designing