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-neutral industries, with a particular focus on the interaction between AI data centers, direct air carbon capture (DAC), district heating networks, and other industrial processes that can effectively
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of impact coverage and model complexity. However, there are still some impacts that remain uncovered and details to existing impact categories can be continuously added. This PhD position aims to investigate
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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monitoring 96 parallel cell-culture experiments under precisely controlled environmental conditions. The system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing
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learning Solid programming skills, preferably in Python and modern deep-learning frameworks such as PyTorch Experience working with large or complex datasets The applicant must be fluent in English, both
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-neutral industries, with a particular focus on the interaction between AI data centers, direct air carbon capture (DAC), district heating networks, and other industrial processes that can effectively
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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, longitudinal modelling, machine learning and multivariate approaches. Proficiency in programming (e.g., MATLAB, Python, or R), handling large datasets, and working with complex analysis 2 pipelines is an
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retaining the reliability, robustness, and safety required for operation in complex real-world environments. The PhD candidate will be supervised by Professor Kristin Y. Pettersen, with Professor Jan Tommy
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with other PhD candidates and postdocs, and opportunities for research to stay with partners, both nationally and abroad, through its international network. This PhD position “Distributed Optimization