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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 23 days ago
processing and will involve EEG and MRI experiments as well as realistic modeling of biological neural networks. It is a cooperation between the TU Berlin, the TU Chemnitz, the Czech Academy of Sciences, and
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on traditional supervised learning techniques (e.g. random forest (RF), artificial neural network (ANN)) will be applied using the parameters of strongest influence on the target properties. Moreover, the obtained
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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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a big plus: Relevant publications (and/or M.Sc. thesis) on the above-mentioned research topics Programming Microcontrollers and Interfacing Sensors Machine Learning Algorithms and Deep Neural Networks
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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of Higher Education and Research (MESR). PINNACLE: Physics-Informed Neural Networks for Accelerated Cloud Light-Scattering Emulation Artificial intelligence is profoundly transforming atmospheric
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neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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-Brain inspired Neuromorphic Nanophotonics (InsectNeuroNano). The long-term vision of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation