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activities. Your Profile: Master and PhD degree in astronomy, physics, computer science or equivalent fields of study. Proven experience in N-body simulations and the comparison of simulation and observation
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astronomy, physics, computer science or equivalent degree Solid background in star and planet formation or small bodies of the solar system Experience in hydrodynamics and/or N-body simulations in the star
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Are you passionate about cutting-edge fundamental research on molecular and 2D materials heterostructures? Are you looking to pursue research in an excellent, interdisciplinary research environment at a leading German university? If so, we invite you to apply! The newly established Collaborative...
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Centre, the Neutron-SimLab or various physicochemical characterization methods and workshops. The opportunity to help shape a new department. Access to the infrastructure of the Institute
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: Analysis and Geometry Appl Deadline: 2025/07/13 11:59PM (posted 2025/06/06, listed until 2025/08/25) Position Description: Apply Position Description In the Faculty of Faculty of Computer Science
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The Leibniz Institute for Financial Research SAFE - Sustainable Architecture for Finance in Europe - (www.safe-frankfurt.de ) promotes scientific research, transfer work to the public and independent policy advice on all aspects relating to the structure and functioning of the financial system....
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direction of research family-friendly, flexible working time models, part-time option, remote work, additional days off on December 24 and 31 corporate health management and sports activities through egym
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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are initially limited to three years. The Microbiome Dynamics (MBD) department contributes to the development of personalized microbiome-based strategies to aid in the detection, monitoring, treatment, and
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and