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computing (SC)? Are you fascinated by the emerging field of machine learning (ML)? Are you our next PhD-candidate in scientific machine learning or SciML (combining SC and ML)? Are you eager to work on the
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of machine capabilities. However, learning these models requires direct access to vast data repositories, which poses significant privacy and logistical challenges, especially in the health sensing domain
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and explainability. Reviewing technical literature on machine learning and explainable AI. Developing a normative evaluation framework for the use of explainable AI in machine vision for autonomous
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of the vertices in multi-dimensional space for machine learning tasks. Policy evaluation is widely used in reinforcement learning, for instance, for training large language models. Your challenge is to develop
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Fully funded (4 years) Ph.D. on: Learning Analytics-based dashboards for supporting students’ blended learning. Proposed start date is 1 September 2024, but a later start date may be considered
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surrogate models. Applying symbolic reasoning to define and analyze system components and their interactions. Merging machine learning with symbolic AI to enhance system (performance) monitoring. Advancing
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people's daily lives. Job requirements This work is at the intersection of between moral philosophy, human-computer interaction and industrial design. Candidates should have a master’s degree in a related
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advancements in computer graphics, leveraging techniques from the filmmaking industry to create visually compelling representations of complex brain datasets. Collaborate closely with renowned neurosurgeons
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? • Are you fascinated by the latest developments in self-supervised learning and generative models? • Are you excited to work on perception tasks for safety-critical systems using the next-generation
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with hardware architecture, FPGA, and system-level simulation is desirable. A good theoretical understanding of statistics and machine learning theory. Strong analytical skills and proficiency in