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contribute to the development of innovative, AI-based solutions for mapping forest disturbances using satellite data and deep learning.. About the position Forests across Europe are experiencing unprecedented
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dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You
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look forward to receiving your application! At the intersection between AI and single atoms. Your work assignments We are looking for a PhD student with a background in machine and deep learning with
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application! At the intersection between AI and single atoms. Your work assignments We are looking for a PhD student with a background in machine and deep learning with focus on image processing and restoration
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using Cell Painting and high-content imaging. Deep learning and multivariate methods, both supervised and unsupervised. Development of software and pipelines for analysis of large-scale image data
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thermodynamics, with an emphasis on both theoretical and practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid
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thermodynamics, with an emphasis on both theoretical and practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid
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Management. We are seeking a highly motivated reserearcher with a deep interest and specialization in studying entrepreneurship using econometric and statistical methods. The candidate should be familiar with
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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, correct batch effects, and preserve biologically meaningful signals. Clinical contextualization: acquire a deep understanding of breast‑cancer pathology and treatment pathways to evaluate how integrated