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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
materials science or a related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic
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related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic potentials • Good
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, developing knowledge, methods, and transferable software for the simulation and comprehensive sustainability assessment of socio-economic systems. Its purpose is to foster sustainable eco-innovation, through
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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understand, explain and advance society and environment we live in. Your role Conduct research and prepare a doctoral thesis in metabolic network modelling and computational epigenomics Develop novel methods