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command of written and spoken English • Experience with qualitative research methods is an asset • Good knowledge of machine learning /data mining in science • Good programming skills in at least one
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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-stage researchers in advanced data analytics, causal inference and machine learning related to health policy topics. Specifically, it is training them to evaluate real-world policy impacts. Focusing
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machine learning Data analysis and advanced statistics Economic and social transformations related to digitization Experince when it comes to programming (preferably Phyton) and in the use of modern tools
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will be made for all categories of stakeholders to support the process of job matching. Where to apply Website https://resurseumane.ase.ro/pnrr-cf_178-jobkg-a-knowledge-graph-of-the-romanian
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experience in Artificial Intelligence (AI) and Machine Learning (ML) concepts, algorithms, and frameworks; Hands-on experience with popular ML libraries and tools (e.g., TensorFlow, PyTorch, scikit-learn
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industrial component given the close collaboration with the quantum computing start-up, Quantum Motion, particularly with Dr. Ciriano-Tejel, machine learning group. Key responsibilities Conduct research
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of the suitability of the profile for the functions and tasks to be performed focuses on the candidate's experience in machine learning and COMSOL modelling of materials and devices. Note: These criteria will be