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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The aim is to develop machine-learning models that describe how
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Models, Knowledge Graphs, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Machine
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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, TensorFlow, HuggingFace). Model Development and Delivery Support Perform data cleaning, exploratory data analysis (EDA), and feature engineering. Train, evaluate, and compare machine learning models under
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, Computer Science, or related field Preferred Experience: Experience with machine learning in medical imaging/biomechanics; grant writing support; clinical gait analysis in clinical/research setting; gross anatomy
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/ Electronics Engineering, Computer Engineering, Computer Science, Robotics, or a closely related discipline, with foundational knowledge in signal processing and machine learning. Working knowledge of computer
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network structures. Methods from graph theory, machine learning, and artificial intelligence will be employed to model complex relational structures and identify patterns in high-dimensional data. The work
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learning or multi-agent systems. Experience with cloud-native technologies (Docker, Kubernetes) or distributed computing. Experience with efficient neural architectures, scalable model design, or resource
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based on machine learning tools for energy problems related to prediction. The application domains include both industry and climate changes. The first two months will be devoted to the study of