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Field
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modeling framework will be designed to support multiple downstream applications, including energy flexibility assessment, advanced control strategies (e.g., MPC and RL), and evaluation of renovation and
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can also be proposed by the candidate as topics of investigations. The exact focus can be adapted to the candidate’s interests. The project offers flexibility to pursue multiple sub-projects while
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cells. Transcription stress acts as a potent trigger of the DNA damage response (DDR), coordinating multiple essential cellular processes. This is particularly relevant in post‑mitotic lineages, where
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will use and develop Python scripts for analysing results and may participate in the development of codes such as the observation simulator and the improvement of the controller. The proposed thesis will
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engineering, or another related field Strong knowledge of Machine Learning theory and methods, and related Deep Learning approaches Excellent knowledge of programming in Python and scientific libraries used
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learning, data science, atmospheric sciences, geophysics, or related fields. Solid numerical modelling and programming skills (e.g., Python, TensorFlow, scikit learn) are essential, along with a basic
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proficiency in Python. Knowledge in Statistical physics and Network science, and interest in complex networks and interdisciplinary research are a plus. The position is for 3 years, and will be located
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Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic
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mini-applications that embody key AI workloads by combining multiple underlying kernels and the key data communications between them. You guide and help analysis and optimization of the performance
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extreme scenarios. Your tasks: Conduct security and resilience studies for highly stressed systems (e.g., multiple faults, high renewable shares, or equipment failures). Evaluate system vulnerabilities and