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for Nuclear Research (NCBJ) is inviting applications for a position PhD student - scholarship (machine learning-driven modeling of metallic glasses) Location: Otwock - Świerk - in the outskirts of Warsaw with
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[maps] and the TUM Garching campus [maps], and all members are affiliated with both institutes. As a PhD candidate in our group, you will drive your own research on machine learning methods in close
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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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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, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning for official statistics. The subject of the thesis will be “Exploring Large
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for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex