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networks or similar machine learning technologies applied to DNA; Preferential: Experience with transcription factor motif discovery; Proficiency in high-throughput sequence alignment methods; Candidates who
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-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision
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-of-the-art models for computer vision based on Machine Learning. - Analysis and Study of existing resources; - Analysis of the state of the art in adversarial attacks and adversarial training and their
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), of the Polytechnic of Leiria Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Undergraduate Positions Application Deadline 6 Mar 2026 - 23:59 (Europe/Lisbon
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Computer Science (or close field) with strong foundations in data management, machine learning, and software engineering. Coursework or projects in NLP/LLMs, information retrieval, knowledge graphs/ontologies, data
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factors: Prior experience in developing algorithms for biomedical image processing (especially aligned with the research group's areas) and machine learning/deep learning techniques. Prior knowledge of data
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 16 days ago
, financed by national funds through FCT/MCTES (PIDDAC Workplan: The scholarship holder will acquire electrophysiological data (EEG; ECG; EMG) from healthy and/or stroke patients, involving brain-computer
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the following conditions: OBJECTIVES | FUNCTIONS The purpose is to continue the research on Machine Learning methods applied to optimization techniques, in particular for the veicule routing problem. The idea is
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: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 40%; VII.II- I – In the evaluation of the interview, candidates' performance
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%; - Criterion 2: Scientific dissemination actions – 40%; - Criterion 3: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 20%; VII.II