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learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development of new data-driven
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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). This exciting translational role sits at the intersection of innovative stem cell engineering and disease modelling, targeting genetic, metabolic, and immune-driven pathologies. You will utilise proven kidney
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student-scholarship holder at the Poznan Doctoral School of Institutes of Polish Academy of Sciences
of Sciences in Poznań receives funding from the OPUS LAP 30 (2025/59/I/NZ2/02573) titled “Hypoxia-Driven Metastatic Potential in NSCLC: Multi-Modal Transcriptomic Profiling in 3D and Orthotopic Models” to fund
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signaling concepts We will develop artificial cells that sense disease-relevant signals and generate regulated outputs, with a long-term focus on adaptive responses in tumor-microenvironment models and
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day one, indepenent thinking is encouraged, and you will get all the support you need to further develop your scientific career. PhD Position 1 - Hybrid Traffic Flow Modelling This PhD focuses
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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combining technology development with process modelling and assessment across the waste value chain. The members of the consortium include 4 other research Universities, 2 Universities of Applied Sciences