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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
communication and information management skills We offer: A varied and challenging job within a high-caliber network from politics, science, business and society Work in national and international networks with
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 6 hours ago
Number: 101227450 LipAgg Doctoral Network project The LipAgg project seeks to unravel the structural complexities of amyloid protein-lipid aggregates and investigate their role in pathological aggregation
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15 Fully Funded PhD Positions in Thermoelectric Materials and Solid-State Cooling (MSCA Doctoral Network, Horizon Europe) We are recruiting 15 Doctoral Candidates (DCs) for the EU-funded Marie
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Max Planck Institute for Multidisciplinary Sciences | Gottingen, Niedersachsen | Germany | 3 days ago
. The Position The selected PhD candidate will participate in the EU-funded HORIZON-MSCA-DN-2024-01 project LipAgg. The LipAgg network brings together partners from five European countries and comprises nine
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to quality, integrity, creativity and cooperation. You have strong interest in communication networks, distributed systems, or cloud/edge computing. You have at least basic programming skills (e.g., Python, C
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Security and Communication Technology. The workplace will be located in Trondheim. About the project The PhD work will be carried out within the Centre for Quantum Communication Networks and Applications
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. The London hub, along with others in the US, is part of the NetSI Global program which is dedicated to engaging and integrating regional network science communities and partnering with regional talent
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 28 days ago
the European Doctoral Network “A Coherent Analysis Framework for Emergence in Biological Systems” (CAFE-BIO) on the project “Proliferating active media”, which aims to investigate the interactions between
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constraints. Physics‑Informed Neural Networks (PINNs) embed PDE residuals and boundary conditions directly into training, while operator‑learning approaches provide mesh‑agnostic mappings from inputs to flow
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variables such as gross primary productivity (GPP) and net ecosystem exchange (NEE) Work with data from PhenoRob experimental field sites, equipped with Eddy Covariance towers, to generate digital twins and