86 algorithm-development-"St"-"St" PhD positions at Technical University of Denmark in Denmark
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this challenge by developing privacy-preserving data sharing and analytics tools, with a focus on “differential privacy”. A core theme will be to balance privacy and utility: ensuring that shared data remains
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Job Description Do you want to be part of a team that is developing novel tools to create genetic medicines and better model systems for testing them? Then join the Precision Medicine Technologies
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applications for a PhD position in the research section Business Development. Our research focuses on the economic and societal implications of green technologies, and we have a particular focus on the green
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international research experience. Responsibilities and qualifications You will develop sampling and characterization methods to analyze the composition of current footwear. This includes applying advanced
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description Machine learning opens up new opportunities to accelerate the discovery of next-generation energy materials by combining predictive and generative approaches. In this project, we will develop neural
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for the development of next-generation CAR T cell therapy for solid tumours. You will work with a wide range of methods, including molecular biology, culture of human T cells, CRISPR multiplexed genome engineering
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on mould concept and production system development. As a Researcher you are expected to have an active role in preparation of project applications to attract national as well as European funding. As formal
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communities to cascading hazards, particularly NaTech (Natural Hazard Trig-gering Technological) events. REUNATECH is a Horizon Europe Marie Skłodowska-Curie Doctoral Network that aims to educate and train the
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quality or safety. You’ll also build valuable connections with industry and academia. The project fosters strategic interdisciplinary collaboration across DTU Food, and DTU Sustain to holistically develop
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them