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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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. Experience working with remote sensing or geospatial data is a requirement Strong programming skills applied to geospatial data, such as satellite data, is a requirement. Experience working with deep learning
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national and international partners. The PhD project will focus on integrating advanced photogrammetric techniques applied to historical aerial imagery with modern deep learning-based image classification
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expertise in the following areas: Machine Learning in general, with an emphasis on deep learning and language modeling Model benchmarking and evaluation pipelines for NLP/LLMs Domain-aware application of AI
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above. Proficiency in scientific programming (Python) and deep learning frameworks. Fluent oral and written communication skills in English. Desired qualifications: Expertise in broader topics in
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defence are eligible for appointment. Solid background in machine learning and scientific profile relevant to the project described above. Proficiency in scientific programming (Python) and deep learning
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. The plan is for the candidate tofocus on Bayesian modelling in close collabora-tion with researchers at the HISP centre that work on complementary deep learning approaches to disease modelling as
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at the HISP centre that work on complementary deep learning approaches to disease modelling as well as on development of plat-forms for running and evaluating prediction models. The PhD candidate will develop
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entails teaching responsibilities within automation of synthetic chemistry and high-throughput experimentation. We seek candidates who demonstrate a collegial approach to teaching and a deep appreciation