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- INESC TEC
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Field
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problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning). ; - Participation in R&D projects with links to real-world or industrial contexts. ; - Relevant scientific
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prototypes, transforming them into useful information to support agronomic decision-making; Apply data processing and machine learning techniques to relevant problems in an agricultural context; Support the
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modelling, data analysis, and optimisation. Experience with machine learning or surrogate modelling techniques applied to offshore engineering problems. Experience in collaboration with the offshore wind and
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developing solutions for locating and manipulating semi-rigid objects or complex geometry. Investigating machine learning strategies with limited data, including the generation of synthetic data in simulation
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to hire one Senior Technician within the framework of the “ Leveraging Interpretable Machine Learning Approaches to Improve Child Health for All: From Single Diseases to Multimorbidity ” project, reference
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for more than 2 years. The selected candidate will perform tasks in the field of Computer Science, Machine Learning and Project Management. The tasks will include, but are not limited to: 1) Development
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Engineering, Informatics Engeneering and related fields Skills/ Qualifications Have experience as a Data Scientist, such as developing Machine Learning and AI-related solutions to predict patterns in