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for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with
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engineering Knowledge of machine learning (deep learning) Basic knowledge of computer science or software development, e.g., using Java, Python, or C++ Good communication and information skills, goal-oriented
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) The Institute of Computer Engineering (ZITI) at Heidelberg University invites applications for one research assistant position with the possibility to do a PhD in parallel at the chair of computer architecture
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PhD student (m/f/d) in the field of chemistry, chemical engineering, materials science or comparable
polyoxometalates Using suitable characterization methods to characterize the synthesized materials Using machine learning tools to tune the synthesis parameters in a feedback loop and enhance the properties
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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project work plan and milestones Your profile Completed university studies (Master/Diploma) in the field of Chemical/Metallurgical/(Mineral) Process Engineering, Data Science, Statistics, Machine Learning
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THE FIELDS OF: ATMOSPHERIC PHYSICS AND CHEMISTRY, ELECTROCHEMISTRY, ELECTROCHEMICAL ENERGY STORAGE (BATTERIES), ELECTRONICS, ELECTRICAL AND MECHANICAL ENGINEERING, HIGH-PERFORMANCE COMPUTING, MACHINE LEARNING
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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knowledge of machine learning (e.g., in the areas of object detection and identification, generative AI, etc.) Good written and spoken English skills (min. level B2) Good written and spoken German skills (min
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developing a digital twin, employing machine learning and numerical computations of atomistic processes. At IKZ, a kinetic Monte Carlo tool has been developed in the programming language julia. This allows a