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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable
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the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site nature-based solutions that enhance local
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on methods such as functional connectivity analysis, brain network analysis, or machine learning; Excellent scientific writing and communication skills in English; Ability to work independently while
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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of machine learning, remote sensing and hydrology to evaluate and validate nature-based solutions that enhance local recharge and support the replenishment of shallow groundwater systems in dryland
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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Your job We are looking for an enthusiastic postdoctoral researcher to expand and strengthen research on "Healthy Lifestyle through Data Science, Machine Learning, and AI". Are you excited about the
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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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, Python, and R. The candidate should have a strong capacity to understand processes underlying pro-environmental behaviour from different perspectives, enabling them to simultaneously understand, use, and