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Field
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of dissertation topics: Developing Remote Sensing–Based Indicators of Landscape State and Change Using Data-Efficient Machine Learning Across Scales Profile of the graduate The graduates have deep theoretical
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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twins, human-centric systems, robotics PhD-E: Optimizing Images Quality and Deep Learning Methods for Vineyard Disease Detection. PhD grantors: University Padova (IT) & Poznan University of Technology (PL
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learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | about 2 months ago
well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You will develop the core AI and data-driven models that transform large-scale
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with implications
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subgroups Support public health policy, prevention and hospital planning Provide meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep
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, or computational imaging; You have strong skills in digital signal processing (DSP), including familiarity with machine learning or deep learning; You have proficiency in scientific programming (e.g., Python, MATLAB