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, Heidelberg and Mannheim, our researchers harness interdisciplinary collaboration to decipher the complexities of disease at the systems level – from molecules and cells to organs and the entire organism
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position can be filled on a part-time basis with a minimum of 32 hours per week. The Planetary Boundary Initiative (PBI) is a growing network of institutions and organizations that aims to develop a
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) offers a position as Research Associate / Postdoc (m/f/x) on basic research on the nonlinear dynamics and statistical physics of complex systems and networks (subject to personal qualification employees
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DevOps Engineer (f/m/d) - Cloud Infrastructure and Data Services / Completed university studies (...
, Apptainer, Kubernetes) # OpenStack cloud platform and its components # Proxmox virtualization platform and its components # Basic knowledge in # Complex and distributed networks (Desgin, protocols, etc
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economic consequences. High self-motivation to solve complex problems Independent way of working Reliable and conscientious working style Basic programming skills (Python) advantageous Very good knowledge
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to study complex biophysical processes on long timescales. We use data-driven methods for systematic coarse-graining of macromolecular systems, to bridge molecular and cellular scales. We work on a
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in the challenges of the energy transition and its economic consequences You have a quick grasp and the ability to analyze and creatively implement complex issues You are proficient in the use
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of virtual and physical computer systems (Windows, Linux) Support of internal network and automation systems including end devices, as well as user rights management Provision of IT support services (e.g
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and qualitative modelling approaches for complex social-ecological systems (SES) of tropical coastal zones in Peru, Colombia and Indonesia; Scenario development and Horizon Scanning – to systematically
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the integration of both Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs). This position will involve training and optimizing these neural networks using Python frameworks, including CUDA