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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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analyses by age, ethnicity, and index of multiple deprivation will be performed. The second stage of the study will involve the analysis of prospectively collected EQ-5D-5L data from a cohort of patients to
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aims to develop responsible transport appraisal methods that consider multiple performance metrics simultaneously, with a special emphasis on fairness to weigh different circumstances, constraints, and
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UTEP's strategic initiative to develop a world-class research cluster in this field, supported by the UT System Regents' Research Excellence Program. Research Assistant Professors will contribute to high
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-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position is funded from
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TV-L, part-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position
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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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better The right place for IMPACT. SnT researchers engage in demand-driven projects. Through our Partnership Programme, we work on projects with more than 55 industry partners Multiple funding sources
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AI training has emerged as the standard approach, which utilizes thousands of high-performance devices within a data center to collaboratively process tasks. When they are even more complex, the
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the health and performance of humans in several situations (e.g. firefighters, who are exposed to extreme heat, or older adults who are exposed to heat stress during heat waves) by modeling the environment