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forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods
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streams—including data streaming to cloud databases, scientific visualization, and integration of machine learning workflows. Development of additional modules within commercial FE software (ABAQUS, MSC
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint for the construction industry and enable
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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with statistical and numerical analysis methods as applicable to strain design problems is a distinct advantage. Familiarity with machine learning tools such as PyTorch, HuggingFace transformers and
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extraction) that can be miniaturized and integrated into portable devices. Perform SERS measurements and data analysis of SERS data (e.g., using machine learning). Develop, test and apply new fiber-based SERS
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will lead efforts to apply state-of-the-art AI techniques (machine learning, deep learning, generative models, etc.) to the discovery and development of new materials in critical domains: water, energy