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Field
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algorithmic aspects related to the development of highly accurate, efficient, and robust AI models capable of operating effectively within complex and dynamic radiofrequency spectral landscapes, accounting
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of the variability of stars. The analysis will involve processing the photometric time series data into photometric light curves and developing algorithms to quantify the variability of the stars. All data will then
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applications for the position of Research Assistant. Key Responsibilities: Study and analyze dataflow patterns in emerging data-intensive applications. Design and develop parallel file systems for node-local
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(E13 TV-L, 100%, starting 1 April 2026 or as agreed) 22.12.2025, Academic staff The Professorship “Algorithmic Governance and Public Policy” (Prof. Daria Gritsenko), invites applications for a
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data-driven analysis algorithms for the assessment of thin-film solar cell fabrication processes within NOMAD Oasis installations. The team is responsible for the installation and development of (meta
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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research on the design and development of next-generation wireless communication systems. Specific topics include, but are not limited to, waveform design for the THz spectrum under hardware constraints
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Transparency). The main tasks will be to: Develop AI-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness
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. Implement AI-driven diagnostics and battery state estimation algorithms. Collaborate closely with industry stakeholders to co-develop next-generation battery control solutions, supporting commercialisation
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on application logic rather than underlying algorithm development), and the capability of independently driving the full "Data-AI-Deployment" process; (c) the ability to evaluate the fit between AI applications