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Associação COLAB TRIALS - Laboratório Colaborativo para a Inovação em Ensaios Clínicos | Portugal | 15 days ago
; Ability to define strategies for outreach and exploitation; Computer skills (ex Microsoft Office) and knowledge or ability to learn how to manage directories/platforms; Basic knowledge of development and
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on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Experience in musical audio machine learning frameworks, advanced knowledge in music theory, and
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programme Reference Number AE2025-0509 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0509
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, for the period from 2021 -2026. Reference: BI/UTAD/116/2025 Scientific area/research field: Engineering Researcher profile: First Stage Researcher (R1) Admission requirements: Degree in Computer Engineering
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of civil protection assets; - Researching and developing possible ways of calculating the risk of exposure of civil protection assets; - Applying machine learning algorithms to assist in risk calculation
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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
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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integrating biomedical, epidemiological, or environmental data. Must show solid skills in computational modeling, multivariate statistics, and/or machine learning. Proven proficiency in the English language
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MSc in Electronic Engineering, Applied Physics, or a related field with a focus on Electrochemical Sensing and Data Science; Knowledge of machine learning methods and programming tools; Experience with
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating