14 distributed-algorithm-"Newcastle-University"-"Newcastle-University" Fellowship research jobs at University of Oslo
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distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will
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English English PhD Research Fellow in Machine Learning and Distributed Data Processing Apply for this job See advertisement Job description Position as PhD Research Fellow in Machine Learning and
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) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high-performing designs
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. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity
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Carbon monOxide Mapping Array Project (COMAP) line intensity mapping (LIM) experiment, aiming to map the large-scale distribution of star-forming carbon monoxide around Cosmic Noon (targeting redshifts
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models Experience in working with subsurface imaging Proficiency in leveraging GPUs and distributed training for large-scale datasets is highly desirable Good background in image analysis/computer vision
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architectures such as vision transformer or foundation models Experience in working with subsurface imaging Proficiency in leveraging GPUs and distributed training for large-scale datasets is highly desirable
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algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and neuroscience. The candidate is
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distribution of star-forming carbon monoxide around Cosmic Noon (targeting redshifts between z~2–3) and the Epoch of Reionization (z~6–8). Currently, the experiment is in the COMAP-Pathfinder phase, which
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using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and