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- National Centre for Nuclear Research
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- Wydział Matematyki Fizyki i Informatyki UG
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the Generative Flow Network (GFlowNet) algorithm. We plan to further enhance this algorithm to consider the shape of biological binding sites and incorporate modules for optimizing physicochemical properties
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automation and simulation-driven optimization of high-frequency systems using machine learning * development of optimization algorithms and numerical modeling procedures * development of simulation models
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on are: • approximation algorithms in QIT (e.g. ground state energies); • symmetry- and size reduction of polynomial optimization problems; • characterization of quantum codes and quantum capacities
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analysis algorithms will be used to assess how different neutrophil subpopulations directly and indirectly kill tumour cells, and how this behaviour is influenced by other cells in the tumor microenvironment
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systems * knowledge of modern artificial intelligence algorithms * practical experience in implementing IT services using artificial intelligence technologies * experience (minimum 5 years) in teaching
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for the design and the benchmarking of algorithms tailored for photonic quantum computation. The outcome of the project will be the assembling of three prototypes: ● QSPACE, machine encoded in spatial modes and
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the Matlab and LabView environments for acquiring and analyzing measurement data, developing algorithms for criterion quantities for assessing the emission and propagation of conducted disturbances up to 150
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skills in designing advanced neural networks (including Vision Transformers, Graph Convolutional Networks, and Spiking Neural Networks), hardware implementation of algorithms for reconfigurable FPGA
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. Application programming in Linux environment. Experience in applying artificial intelligence algorithms. Proven knowledge (e.g., certificate, employment certificate as a designer) of CAx systems. Verified
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/undertaking dr hab. Szymon Toruńczyk Description of programme/project/undertaking The combination of methods from logic and graph theory has been extremely successful in the design of algorithms, in complexity