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Field
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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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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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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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(Mandatory): Machine Learning Statistical Modeling Data Visualization Academic Writing Languages (Mandatory): English: IELTS 7.0 or higher LanguagesENGLISHLevelExcellent Research FieldComputer science
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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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: comparative omics, genetic diversity analysis, mathematical modelling, machine learning, and the use of model organisms. Develop transferable skills such as scientific communication, project management
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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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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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or pre-printed at least one primary research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning