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institutions, and a research and development provider for numerous companies throughout the world. The INM is a member of the Leibniz Association and has about 250 employees. The INM Research Department Energy
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bases and instruments in terms of their efficiency and incidence Applied game-theoretic modelling based on 1) and on numerical estimates of the benefits that potential donor countries derive from
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, or similar disciplines Graduate students expecting to receive their PhD within six months can also apply Experience in the advanced analysis of genetic or proteomic data Interest in learning methods
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`s degree and PhD in quantum physics, computer science, electrical engineering, mathematics or a related field Experience in quantum computer programming Experience in applying numerical methods and
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
related field # Proficiency in programming languages (Python, C/C++, Julia) # Background in machine learning methods # Experience in developing, training, and tuning machine learning models # Prior exposure
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department collaborates with numerous national and international partners and with local clinical and research departments. We offer a dynamic, interdisciplinary environment with extensive training
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methods to estimate the uncertainty in the daily predictions What do we expect from you? We seek a scientifically curious researcher who is passionate about understanding the environment. You should hold a
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ecosystem models. Experience using high-performance computing systems. Proficiency in running numerical ocean models. Familiarity with operating systems such as Linux/Unix and proficiency in shell scripting
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analysis of in situ and remotely sensed marine optical data. Experience with advanced statistical methods for analyzing complex ecosystem and environmental datasets. Proficiency in spatial data analysis
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, methods, and algorithms into existing high-performance frameworks, the fast prototyping of new ideas in individual code, an interest in the entire simulation pipeline: starting from simple algorithms