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Field
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for the development of the experimental platform, as well as benchmarking new techniques and protocols for implementing quantum algorithms targeting industrially relevant problems ranging from optimisation to quantum
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in algorithmic game theory and online learning the opportunity to work with Prof. Celli, developing key skills and gaining access to the research networks of the BIDSA center and the Department
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integration; (b) conduct algorithm research and development in areas such as computer vision, multimodal learning and embodied AI; (c) conduct experiments, data collection and performance evaluation; (d
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property somewhat specific to nuclear systems amongst all many-body quantum systems. The project involves developing quantum algorithms for implementation on near-term fault-tolerant quantum computers
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Engineering or related fields. Be enrolled in a doctoral program in the scientific area of Electrical Engineering. Willingness to work in the developing of signal processing and machine learning algorithms
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how such generalizations can be used to obtain efficient algorithms for geometric intersection graphs. The project will be supervised by prof. Mark de Berg. Information As a PhD student working on
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) experience in large model training, knowledge distillation or efficient deep learning algorithm development, foundation model implementation and optimisation; and (c) good communication skills in English
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resource. The project will combine algorithm development with hardware-aware modelling and evaluation on FPGA-based platforms. The successful candidate will have the opportunity to help define new approaches
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contributing to the design and development of advanced models, algorithms, and AI‑driven systems for renewable energy microgrids and data centers. The Research Assistant will work closely with laboratory
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. The position will focus on developing computational algorithms and tools for the analysis of mass spectrometry-based proteomics data. Research projects will center on advancing the FragPipe computational