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Researcher (R2) Positions PhD Positions Application Deadline 12 May 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Not Applicable Job Status Not Applicable Offer Starting Date 20 Apr 2026 Is
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techniques and machine learning algorithms. The goal is to decipher patterns in the bioelectrical signals. Project UID-50008-2025 funded by FCT-Fundação para a Ciência e a Tecnologia. Scientific Area: Basic
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 days ago
17 Apr 2026 Job Information Organisation/Company Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID Department DRH Research Field Engineering » Civil engineering
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: Baiqiao Chen (ist33735) Co-advisor(s): Baiqiao Chen (ist33735), Instituto Superior Técnico. Organic Unit: Scientific Area of Naval Architecture and Marine Engineering Scholarship Theme: Machine learning
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Portuguese and English. 3. Preferred requirements: d)Be enrolled in, or meet the conditions to enrol in, a PhD student in Education, with a specialization in Educational Technologies. e) Experience in
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form, available at https://www.fct.unl.pt/en/faculdade/concursos/investigadores . The application process must be submitted in Portuguese or English. The application should be submitted by e-mail, and
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Description NOTICE OF ONE PhD Research studentship, for the project “LEARNING BOLSTER”, (2023.17516.ICDT), AT THE CENTER FOR PSYCHOLOGY OF THE UNIVERSITY OF PORTO (CPUP) IN THE FACULTY OF PSYCHOLOGY AND
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-472StreetRua Escola Industrial e Comercial Nun'Álvares, 34 Contact City VIANA DO CASTELO Website https://www.ipvc.pt/pt/ Street Rua Escola Industrial e Comercial Nun’Álvares, 34 Postal Code 4900-472 E-Mail
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scientific area of the call; and other reasons why they consider they should be selected; e) Proof of enrolment in a study programme leading to a PhD degree in Education 1; f) Certificate of Qualifications 1
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LiDAR data (GEDI and ICESat-2), and generate time series; e) Apply deep learning (DL), machine learning (ML), geospatial foundation models (e.g., AlphaEarth, TESSERA), statistical inference (uncertainty