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<span lang="en"> Helmholtz-Zentrum Hereon</span> | Geesthacht, Schleswig-Holstein | Germany | about 10 hours ago
Material and Process Design at the Helmholtz-Zentrum Hereon is offering a 4-year PhD position in the field of computational materials engineering, machine learning, and digital twins for advanced
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a
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background in machine-learning and FPGA programming and be familiar with mixed-signal integrated circuit design.
What we offer:
- The possibility to
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Master Thesis - Decoding Occupancy: Classifying User Activity from Building Power Consumption with Machine Learning At the Institute of Climate and Energy Systems Engineering (ICE-1) we focus on the
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<span lang="en">Leipzig University</span> | Leipzig, Sachsen | Germany | about 10 hours ago
energy that drives the particles
- Track hundreds of interacting particles with machine-learning methods and test fundamental thermodynamic bounds on control
- Work closely with the Tel
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clinical data, imaging, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction
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at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development.
Tasks of the PhD Student
- Develop -
Helmholtz Association of German Research Centres | Geesthacht, Schleswig-Holstein | Germany | 2 months ago
), including uncertainty estimates. You will work on extending neural processes to large-scale, multimodal geospatial data, a setting that remains largely unexplored in current machine learning
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<span lang="en">Universitätsklinikum Hamburg-Eppendorf</span> | Hamburg, Hamburg | Germany | 12 days ago
>Your tasks
You will develop computational and machine learning methods to analyse and integrate spatial transcriptomics data of different resolutions and technologies, to characterise
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
. Strong scientific programming skills, preferably in Python, and experience with modern software-development practices Experience in implementing machine-learning and deep-learning methods Excellent