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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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. The successful candidate will be joining the Quantum Optics Theory group led by Prof. Dr. Maciej Lewenstein. The successful candidate will work on Machine Learning research. Share this opening! Use the following
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Description Develop the doctoral thesis focused on Quantum Computing and Machine Learning for Power Systems Where to apply E-mail monica.aragues@upc.edu Requirements Research FieldEngineering » Industrial
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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of machine learning tools in the process industry - General research tasks (scientific article writing, oral presentation of results, document management, etc.) - Technoeconomic analysis and life cycle
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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or equivalent Skills/Qualifications A Bachelor's Degree or an equivalent in Computer Science, Telecommunication Engineering, or a related field with a strong academic background in Machine learning, Natural
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or equivalent Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications Skills in acoustics (PhD in acoustics required) and acoustics software. Skills in machine learning and deep
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Skills/Qualifications Technical Competences: Programming: Python, Java, JavaEE, JavaScript, R, Android. Machine learning/AI frameworks: PyTorch, TensorFlow. LLMs (Large Language Models): Prompt Engineering