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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved
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21 Nov 2025 Job Information Organisation/Company INESC TEC Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1
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should possess a strong background in advanced computing and data science, machine learning, or in a related field, with expertise in monitoring data reliability, quality assurance, and AI modelling
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Universidade Lusófona´s Research Center for Digital Human-Environment Interaction Lab | Portugal | about 2 months ago
Python Machine Learning Libraries (PyTorch, Keras, etc.); statistical and machine Learning expertise and supervised and unsupervised methodologies; Valuable Bonus Skills include: experience with Unity3D
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, and clustering methods; Knowledge of machine learning approaches for classification and patient stratification is valued; Postdoctoral experience in the appropriate field, with research outputs ideally
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
1801P.01460.1.06 LARSYS/ISR BASE 2025-2029 - LASEEB LAB/ISR, financed by national funds through FCT/MCTES (PIDDAC Workplan: The scholarship holder will acquire simultaneous EEG/fMRI imaging from healthy
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of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
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-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: 1. Formulate and validate automated inspection methodologies based on computer vision and AI techniques
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related to Computer Science, Machine Learning, Data Science, Information Management or other related areas; Have skills in the development and application of machine learning models in supervised and non
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tools, with particular focus on multi-threaded and distributed scenarios. Experience with observability tools, particularly OpenTelemetry. Solid knowledge and experience in machine learning, deep learning