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focuses on modeling IoT-Fog environments, designing multi-objective optimization algorithms (latency, energy, reliability), and developing strategies for critical IoT applications like smart cities and
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. This position is to be filled at the Institute of Climate and Energy Systems - Energy Systems Engineering (ICE-1), where we develop models and algorithms for the simulation and optimization of future energy
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of reinforcement learning and popular modern reinforcement learning algorithms. Students will develop familiarity with both model-based and model-free reinforcement learning algorithms, including Q-learning, Actor
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, transcriptomic, and proteomic datasets using open‑source bioinformatics software. The successful candidate will apply bioinformatics algorithms and statistical methods to interpret, analyze, and visualize large
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We are seeking a Research Associate to support research and development in grid-forming converters, power electronics, and advanced energy systems. The role involves designing, modelling
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
development and evaluation of algorithms and software systems that advance these practices in support of artificial intelligence. Education and Experience: Experience implementing informatics/bioinformatics
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within a Research Infrastructure? No Offer Description Group Leader in Quantum Algorithms and Machine Learning (f/m/x) Ref. Number: MAB/06/2026 Location: Warsaw, Poland Salary: 20 750 - 24 250 PLN/month
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. • Strong analytical skills and scientific rigor. • Ability to work at the interface between algorithm development and experimental validation. • Capacity to collaborate effectively in a multidisciplinary
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positions within the Novo Nordisk Quantum Computing Programme (NQCP). The NQCP mission to develop fault tolerant quantum computing processors requires the interplay of several research teams. All NQCP
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of the thesis is to develop an integrated framework for evaluating ecosystem responses to emerging blue bioeconomy activities under different management scenarios. To achieve this, the study combines in situ