30 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships at Forschungszentrum Jülich in Germany
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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neuroscience is essential Experience with modelling, analysis of complex dynamical systems, simulation, analysis of large-scale datasets with machine learning methods, and software development are beneficial
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sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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and sensor data processing, bioinformatics, systems biology, or biophysics. Familiarity with simulation environments, numerical methods, or machine learning approaches is an advantage. Fluent command