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approaches, technologies and state-of-the-art research, as well as the systematisation of knowledge relating to the establishment of forest stands by direct seeding; Technical and scientific monitoring
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criteria: - Criterion 1 – Absolute merit of curriculum vitae; - Criterion 2 – Specific knowledge related with the work plan; - Criterion 3 – Adequacy of the candidate’s profile to the work plan to be
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Participation in teaching activities Supervision of Bachelor's and Master's students Your Qualifications: The successful candidate must hold a completed Diploma or Master's degree in Human Medicine Knowledge
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assignment of a research grant, with one position(s), under the project CENTRO2030-FEDER-02359100, title Nutristress - New nutraceutical formulation to prevent cognitive decline caused by stress. The call is
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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | about 2 months ago
criteria: Curriculum Vitae (20%); Bachelor's Degree Grade (30%); Master's Degree Grade (20%); Knowledge/experience in GIS, Spatial Modelling, and InSAR (20%); Motivation Letter demonstrating compliance with
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - broaden knowledge of the state of the art in the specific scientific field
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awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - knowledge of complex production systems
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) their evaluation in cellular processes. Knowledge of (i) cell culture, as well as (ii) characterization via immunofluorescence and qRT-PCR or (iii) nanotechnology, will be considered an asset. V - Initial grant
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conducting trials to evaluate essential agronomic characteristics c) Good level of knowledge on informatics and statistics; d) Proficiency in Portuguese and English, written and spoken; e) Availability to work
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic