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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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activities, i.e., teaching and supervision of BSc and MSc student projects at DTU. We are looking for candidates with Strong skills in AI, Machine Learning, and/or Data Science, preferably with experience in
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote