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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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Description The Research Training Group (in German: Graduiertenkolleg, GRK) RTG2670 “Beyond Amphiphilicity: Self-Organization of Soft Matter Via Multiple Noncovalent Interactions” in the second
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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focus will be on developing compact, efficient, and real-time LLM algorithms/hardware on the edge and developing demos for specific applications such as speech disorder therapy. Your responsibilities will
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Approximation. Parameterized Complexity is a vastly growing area within theoretical computer science that allows for the development of exact and approximation algorithms for computationally hard problems by
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demonstrated experimental realizations and proven theoretical advantages. The project may involve several aspects, including mathematical theory, algorithm development, error correction, adaptation of GBS-based
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the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks