59 postdoc-molecular-dynamics-simulation PhD positions at Technical University of Denmark in Denmark
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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About us You will join an international and highly
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks
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cutting-edge data science? And would you like to be part of a newly formed research collaboration between DTU and Novo Nordisk? Then you could be our new Postdoc. Read on to learn more! About the PhD
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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of non-traditional sensors for pavement monitoring, such as accelerometers, gyroscopes, and dynamic tiltmeters. Lastly, parametric investigations will be carried out with the validated code, utilizing as
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projects partners. This requires good communications skills but also allows you to co-operate with leading European research institutes. Responsibilities and qualifications You will be part of a dynamic work
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of thermodynamic modelling, process simulation is an advantage. For applicants to the PhD position, you must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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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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global water contamination through advanced materials science. Your Role: Investigate the structural and chemical dynamics of molecular adsorption processes using advanced characterization techniques