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worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability of the developed prototype hardware implementation. Qualifications: PhD in Electronics/Computer Engineering
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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, including fitting and manipulation of large array-type data sets (using Python, Matlab or equivalent) Ability to communicate well, and work within a collaborative team environment Preferred Knowledge, Skills
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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Proteomic and metabolomics analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via