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Field
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. The group will be contributing to the Physics Modeling (MC software, MC validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous
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the most informative polarimetric parameters and optimal wavelengths for detecting abiotic stress. This approach will contribute to more accurate digital phenotyping, supporting sustainable crop monitoring
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and optimization of the process. Comminucation with partners. Dissemination activities and reporting. Supervise master’s and/or PhD students to a certain extent Possibility to engage in teaching at
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biological, biochemical as well as -omics and bioinformatics techniques Optimize and apply ribosome profiling and single-cell ribosome profiling on organoids and clinical samples Characterize the impact of gut
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assessment of CCUS value chains Reactor design, optimization, and sizing using phenomenological and/or CFD methods DFT and Molecular modelling linked to CCUS Strong analytical and problem-solving skills, with
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(or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply
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, advanced computational techniques, and data science. The project involves: Materials Development Platform that will enable redox molecule optimization via predictive simulations, database management, and AI
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optimization tasks at the physical (PHY) and medium access control (MAC) layers, laying the groundwork for compact, locally deployable, and adaptive foundation models capable of sustainably addressing
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of vegetation–climate–technology interactions to optimize energy yield in environments subject to climatic stress. Responsibilities: Monitoring PV panels: temperature, efficiency, dust accumulation. Using
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computational approaches to biological systems. Its core activity is the development of deep learning methods for protein design and optimization, with applications in biology and medicine. - activities: We