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projects with strong integration of the three pillars, i.e., Digitalization, Resilience and Agility, and Green Supply Chain. Preferred to having a PhD in a related technical area. Led or involved in strong
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 15 hours ago
safety of industrial processes. The Department of Thermal Energy and Process Technology is looking for a PhD Student (f/m/d) Dynamic Process Modelling and Value Chain Assessments for Sustainable Methanol
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the Job related to staff position within a Research Infrastructure? No Offer Description Doctoral Position in Food Waste Supply Chains We are looking for a PhD student with joint appointment in ETH’s (i
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or related areas. Enrolment in a PhD programme. Basic knowledge of supply chains/logistics. Strong organizational skills, result-oriented mindset, and ability to work effectively in a team. Proficiency in
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polarization aspects. Under the supervision of the founding team, you will contribute to transferring developed concepts toward industrialization and provide occasional support to the Application R&D team. Main
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projects with strong integration of the three pillars, i.e., Digitalization, Resilience and Agility, and Green Supply Chain. Preferred to having a PhD in a related technical area. Led or involved in strong
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Navigating the Path to Commercial Fusion: Assessing Supply Chain Challenges and Inclusive Growth of Commercial Fusion Energy in the Trent Supercluster (Associate CDT project at the University
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SU-2-1 Futureproof energy and materials feedstock resilience for sustainable aviation fuels supply chain Green Industrial Futures Centre for Doctoral Training (GIF CDT) PhD Research Project Directly
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policies), and global scales (e.g., trade and supply chains), along with broader societal impacts on health and employment. Individuals with expertise in relevant modeling methods are encouraged to apply
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detected at a regional scale. The implementation of advanced InSAR processing chains will provide new insights into the phenomena observed and enrich the databases required for deep learning methods