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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a
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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large
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Caribbean populations of African descent. Most mechanistic insights derive from non-representative cohorts, limiting biomarker discovery and therapeutic precision. Recent multi-omic and machine learning
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development. Demonstrated experience developing applications interfacing with machine learning libraries like PyTorch or ONNX. Demonstrated experience working with large-scale data processing and deployment
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the dedicated section here: https://www.iit.it/en/work-at-iit Where to apply Website https://app.ncoreplat.com/jobsharingredirect/777388/generative-ai-machine-learn… Requirements Additional Information STATUS
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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communication tailored to their needs. For further information, see: https://www.physik.uni-kl.de/oscar/ Course organisation During the research work, the PhD student has the possibility to participate in
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of areas, including AI and machine learning, cloud and mobile computing, computer system and information security, evolutionary computation, computer vision and graphics, and bioinformatics
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quality monitoring, and/or nature-based water treatment designs. In addition, the candidate should have some experience in AI, machine learning, and/or managing large data sets related to water resources