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Field
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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[maps] and the TUM Garching campus [maps], and all members are affiliated with both institutes. As a PhD candidate in our group, you will drive your own research on machine learning methods in close
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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and a heterogeneous emerging computer architecture, collaborate regarding compiler and other tools as well as modeling their hardware for integration into the emerging computer architecture framework
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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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nano-computed tomography, and scanning electron microscopy design and implement deep learning models to enhance resolution of large field-of-view imaging techniques integrate imaging data across
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or Python) • Good knowledge of the English language • Experience with statistics, machine learning, Deep Learning and AI are an advantage • Familiarity with fundamental ecological concepts and experience in
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Models for Real-World Healthcare (MLCARE project DC3). The position is funded by the MSCA Doctoral Network MLCARE (Machine Learning Computational Advancements for personalized mEdicine). The MLCARE