92 parallel-computing-numerical-methods "Simons Foundation" PhD positions at Technical University of Denmark
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well as abroad. To support your success, you will have access to DTU National Food Institute’s excellent laboratory facilities. Your overall focus will be to assess if the current biocide assessment methods based
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
leading universities, research institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its
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. Your research will contribute to sustainable, resilient, and efficient healthcare systems. The research is expected to employ methods from Operations Research, Management Science, Data Science, and
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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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(particularly for packaging), and analytical techniques Experience in packaging processing technologies (e.g. extrusion, Injection, compression molding, others) Familiarity with relevant methods such as
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application phases. This will involve toxicological testing (e.g., cytotoxicity, mutagenicity) and evaluating microplastic release and ecotoxicity using standardized and novel methods (in collaboration with a
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, might be for you! Responsibilities and qualifications Working with colleagues in the MULTIBIOMINE project, you will develop computational methods that use novel strategies to uncover hidden features in
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with methodologies such as AI-assisted evidence synthesis and quantitative health impact assessment and become part of an interdisciplinary research environment with strong links to DTU Compute and the
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data