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-machine interaction, algorithms, training data, bias etc.) and express willingness to increase their knowledge and technical proficiency through individual studies and relevant coursework. Fluency in
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in Physics, Mathematics, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. • Experience in data-reuploading quantum algorithms
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, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable
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quantum algorithms, optical quantum computing and quantum machine learning (highly desirable). • Experience in machine-learning schemes based on arrays of nonlinearly interacting mechanical oscillators
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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid
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and implementation of parton-shower algorithms and their matching to higher-order pertubative computations in Quantum Chromodynamics. Additionally the analyses of the results in the context of the LHC
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electrochemical phenomena. · Propose non-invasive diagnostic methods based on this digital twin. · Develop algorithms to speed up or standardize the determination of the parameters of this digital
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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Your profile You have, or will shortly
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industries. Challenges include algorithmic bias, data privacy, and the erosion of trust in digital environments. Research questions include: How can design and creative methodologies foster transparency and
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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also