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at highest risk of kidney function decline, aiding trial prioritisation What you will learn and why it matters This PhD provides a rare skill set sought after in academia, biotech, and healthcare innovation
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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at the Medical Faculty of Heidelberg University, in collaboration with Prof. Dr. Irmela Jeremias, invites applications for a PhD student in Bioinformatics / Computational Biology as part of the CRC1709-funded
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
31.07.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, is inviting applications for a fully funded PhD position at the Technical University
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the Cardiovascular System”, the SFB1531 “Damage control by the stroma-vascular compartment” and the LOEWE Center “Lipid Space”. The Epigenetics/RNA working group, led by Prof. Ralf Brandes, is investigating how
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experimental research in nanoparticle catalysis using advanced operando electron microscopy This collaborative PhD project between Technical University of Munich (TUM) ( the group of Prof. Barbara A.J. Lechner
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to the success of the whole institution. At the Faculty of Chemistry and Food Chemistry, the Chair of Theoretical Chemistry offers a position as Research Associate / PhD Student (m/f/x) (subject to personal
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Your Job: At the Electrocatalysis department of Prof. Karl Mayrhofer, we offer a PhD position within the team Nanoanalysis of Electrochemical Processes. Lead by Dr. Andreas Hutzler, the team is
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create