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Field
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applicable). Eligible international students will need to make up the difference in tuition fee funding (Band 3 for the year 2025/26). The student will receive a standard stipend payment for the duration
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field that provides a sufficient degree of background in computer science, artificial intelligence, mathematics and data science. Fluency in English, Python, and C/C plus plus are required. Experience
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 4 hours ago
3D vision topics such as 3D mesh models, statistical shape modeling and articulated pose estimation Solid ML/AI foundation Strong programming skills in Python (CUDA is a plus) Hands-on experience with
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. The initial stages of the project will include establishing sample fabrication protocols using different growth techniques, such as sputtering deposition, thermal evaporation, and e-beam evaporation. The PhD
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areas, which will be used to train and assess boundary layer neural network models. The student will develop and evaluate suitable ML architectures, analysing the trade-offs between different modelling
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trigger redesigns across multiple groups. The challenge is compounded by the fact that each discipline uses different data models and representations, making system-level interdependencies difficult
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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of machine learning such as robustness, fairness, and accessibility. You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results
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on plants, the student will explore for the first time the relationship between different TE classes on the ‘genome ecosystem’ of their plant hosts at scale. TEs, like any other group of organisms, have a
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, transcriptomic, proteomic, and metabolomic data 2) Integrating publicly available multi-omic datasets as well as datasets produced by consortium partners to identify hallmarks that describe different dimensions