87 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Technical University of Denmark in Denmark
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with a wide range of data formats and engaging with data experts and database managers. The second major focus is advanced data analysis and statistical modeling to identify patterns in fish distribution
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. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with correspondingly skilled
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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(ORR), oxygen evolution reaction (OER), and carbon dioxide (CO₂) reduction. Collaborating with theoretical research groups to guide the design of active site structures through computational modelling
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circular, economically viable future for packaging. Through SSbD assessment in collaboration with the consortium, experimental work and risk modeling, you will help uncover the hotspots in the production
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, or biophysics. Experience with experimental organic chemistry, NMR, kinetic modelling and/or cheminformatics are advantages. The candidate must be able to work independently, but also participate in
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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for sustainable production of food by applying your microengineering and material science skills? Here, we can offer you a unique opportunity to do exactly that in a dynamic research environment. In the SOLARSPOON
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wearable and ambient IoT sensing systems for activity and health monitoring. Implementing embedded AI models for anomaly detection and behaviour analysis. Working on digital twin and serverless IoT
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence