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Field
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collaborative and international projects Experience/knowledge in HIL systems Hands-on lab experience and/or interest Knowledge on Machine Learning, or other AI techniques Team Worker Initiative in Research and
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statistics (regression methods, time series analysis, multivariate analysis, clustering methods), machine learning techniques (neural networks, random forests, decision trees, SVMs, etc.) and optimisation
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Requirements Educational Requirements: Artificial Intelligence. Deep Learning. Computer vision. Reasoning methods based on first-order logic. Design of complex computer systems. Professional Experience: In LLM
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and Hugging Face. – Working with machine and deep learning models, including recurrent neural networks (RNNs), transformers and embedding techniques (Word2Vec, GloVe, BERT). – Designing and
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meetings at the University of Alicante. 2.6. Tasks to be carried out: Design and development of machine and deep learning models. Processing of volumetric data and images. Extraction of biomedical markers
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scale. Co-develop, apply and validate machine learning models from basin to regional scales. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for-recruitment-of-research-s
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physicsEducation LevelMaster Degree or equivalent Skills/Qualifications A Master’s Degree in Physics or similar. Specific Requirements Experience in big-data management and analysis, with solid machine learning
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for developing AI algorithms: NumPy, Pandas, Matplotlib, PyTorch. Development and implementation of machine learning and AI algorithms based on neural networks and deep learning. 3.3. Additional education (maximum
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ACTIVITIES IN THE RESEARCH AREA OF DEVELOPMENT OF AN ADVANCED PLATFORM FOR THE DIAGNOSIS AND TREATMENT OF NASAL OBSTRUCTION USING 3D MORPHOMETRY, FLUID MECHANICS AND MACHINE LEARNING AUTOMATIC. (C-059/25
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phenotypic ontologies (HPO) and phenotype-genotype analysis. Knowledge of machine learning tools applied to genetic analysis. LanguagesENGLISHLevelExcellent LanguagesCATALANLevelExcellent