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Postdoc in Machine Learning for Oligopeptide Design (1.0 FTE) (V24.0584) « Back to the overview Job description The advent of modern machine learning (ML) methodology is accelerating scientific
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the “Translator” work package within the core research programme of CropXR. Our goal is to develop computational methods to translate integrated plant models between species, combining machine-learning inferred
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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and
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of making in ancient Roman visual culture (e.g. depictions of craftsmen at work, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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composition, processing, and consumer responses. The AI system developed will utilize advanced techniques such as machine learning, natural language processing, and predictive modeling to detect potential
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, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML (Random Forest, SVM, Fully Connected Neural Networks) will be essential
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), machine learning, network analysis, and econometrics. Proven ability using industrial ecology methods, such as LCA, MFA, and (MR)IOA to assess the scale and drivers of social, economic and environmental
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related field. You have a strong background in quantitative research methods, including statistical modelling, data analysis, machine learning, and/or GIS analysis. You have proven expertise in climate
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analyzing textual and visual content with supervised machine learning. Conducting focus groups with social media users to understand how this content may shape beliefs and attitudes about AOMs, a healthy diet