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Description Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms? Job description Online learning algorithms achieve robustness often at the expense
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across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks
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many approaches in explainable AI and neuro-symbolic methods focus on similar issues, they are prone to learning concepts with the wrong intended meaning, especially when different concepts
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topics broadly cover how companies build and retain sustained competitive advantage in the face of current business- and societal challenges. Overall, we are looking for a candidate who is eager to learn
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skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A certain affinity towards turning complex concepts into real-world practice is desired. The successful candidate is
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flows, or reinforcement learning-based design optimization. Strong programming skills in Python with experience in PyTorch, JAX, or equivalent deep learning frameworks. Ability to work independently
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are evolving along with it. At Maastricht University, your work makes a difference — whether you explore scientific frontiers as a researcher, inspire students in the classroom, or help shape the innovations
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). Experience applying statistics and Machine/Deep Learning to real-world data. Experience working with manufacturing process data, robotics systems and/or metrology data (sensor data, quality data, measurement
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling