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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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or reinforcement learning Interest in (or willingness to learn) plant physiology, genomics and process-based modelling; any prior exposure to crop or ecological models is a plus Proven ability to work independently
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and curate LC-MS/MS data for high-quality feature extraction Design and train machine-learning models for mass spectrometry and chemometric data Integrate multi-omic data including genomics and
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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processes that produce energy and raw materials. The Institute of Radiopharmaceutical Cancer Research develops radiotracers for imaging of cancer biomarkers and personalized therapies. In a joint effort of
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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Your Job: We are offering a PhD position dedicated to the advancement of cryo-EM image analysis methods at the interface of Structural Biology and Electron Imaging at the Forschungszentrum Jülich