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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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workflows and benchmark methods (simulation + real datasets) Develop rare-event–sensitive CIN/aneuploidy metrics and validate them across multiple cancer cohorts Link CIN programs to outcomes and therapy
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. Hands-on experience in fermentation, microbial cultivation, and analytical techniques (HPLC/GC). Familiarity with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and
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programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin. Instructions
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degree: Master's (or equivalent) in neuroscience, psychology, biomedical engineering, computer science, or related discipline. • Skills: Python programming, solid knowledge of statistics, behavioral
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interest in quantitative biology Curiosity and motivation in learning multiple -omics approaches Preferred experience includes familiarity with quantitative proteomics or NGS library generation Previous
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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field strong analytical skills, with knowledge of fluid dynamics, aerodynamics, and numerical simulation methods basic programming skills (e.g., Python, MATLAB, or similar) and familiarity with data
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bridging digital modelling with real-world factory implementation, this project will contribute practical methodologies and guidelines for scalable, circular manufacturing systems across multiple industrial
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two PhD students for this project. Your work focuses on integrating multiple sources for prediction, including common and rare genetic variants, family history, ancestry and typical age of onset. Your