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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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the state of soils and waters. The department is responsible for several important research infrastructures in the form of soil chemistry and soil physics laboratories, a computer tomography, a lysimeter
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, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and collaborations with experimental
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academia and industry. Requirements The following qualifications are required: Solid knowledge in mathematics and statistics, in areas such as linear algebra, probability theory, machine learning, high
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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. More information about the total announced post-doctoral positions within in
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on developing methods for the verification and validation of systems that embed machine learning or generative models, addressing challenges such as non-determinism, data drift, and explainability. The project
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particularly valuable. Documented experience with machine learning and biostatistics is also highly meritorious.You can find information about education at postgraduate level, eligibility requirements and
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may