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. The project employs machine-learning techniques to build a novel global dataset of georeferenced environmental protest events for the period 1990-2025. Together with global datasets on climate change and
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this end it will utilize NLP (Natural Language Processing), machine learning and explainable methods for knowledge discovery from heterogeneous data sources (including multilingual data). The intention is to
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prevention. To this end it will utilize NLP, Natural Language Processing, machine learning and explainable methods for knowledge discovery from heterogeneous data sources (including multilingual data
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on scientific excellence and experience in the field of biomass fractionation, or cellulose chemistry. Experience in data mining and machine learning for process optimization will be an added advantage. Terms
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. Experience in data mining and machine learning for process optimization will be an added advantage. Terms of employment The position involves full-time employment for a minimum of two years and a maximum of
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to, quantum information theory, quantum error correction, tensor networks, machine learning, quantum algorithms, quantum control, the interface of quantum information and many-body theory, non-equilibrium
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, and dynamics of these fascinating proteins, by combining methodology from computational, biochemical, biophysical, machine learning, and structural techniques. The PhD candidate will work closely with
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of these fascinating proteins, by combining methodology from computational, biochemical, biophysical, machine learning, and structural techniques. The PhD candidate will work closely with other PhD students, postdocs
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. The surrounding circle of ideas includes quantum computers, machine learning, and “big data”. The WINQ initiative constitutes a sizable investment in theoretical physics that complements the strategic contributions
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microscopy polyclonal epitope mapping applied to infectious and auto-immune diseases”. The Department of Biochemistry and Biophysics offers a 4-years PhD position to study “Cryo electron microscopy polyclonal