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Field
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Description Implement and validate machine learning models and statistical algorithms for data imputation, anomaly detection and uncertainty management in geospatial environments. Collaborate in the preparation
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learning. Professional Experience: Have worked using Machine learning. Programming in C, C++. Experience in functions similar to those described will be valued, specifically in the development of research
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fields. Relevant Training or Courses (optional): Experience with R or similar programming tools, or machine learning algorithms. Geographic Information Systems (GIS). Basic and advanced statistics, time
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projects using NGS techniques applied to oncology. The candidate will mainly carry out the following tasks: • Analysis of high-throughput omics data with state of the art methods including machine learning
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.), command-line tools (bwa, samtools, bedtools, etc.) and biological databases (cBioportal, COSMIC…). Fluency in R and/ or Python. Proficiency in English. Knowledge of statistical modelling or machine learning
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processing (computer vision & machine learning) Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » OtherEducation LevelMaster Degree
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Computer Science PhD program at the Universitat de Barcelona. https://www.ub.edu/escoladoctorat/en/access-and-admission/access-pathways-and-requirements - Machine/deep learning - Excellent programming skills
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of multimedia datasets (voice, text, etc.). Development of predictive models for cognitive impairment and Parkinson's disease using signal processing and machine learning techniques. Development and debugging
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the project ‘MultiLearn: A multimodal approach to language learning: a window into the linguistic skills of children with neurodevelopmental disorders‘. The project is led by researchers Alfonso Igualada and
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analysis, including statistics and machine learning techniques. Proficiency in scientific programming, especially Python and/or Matlab, and experience in shell scripting. Excellent written and oral