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contract may not be renewed. Applications must be formally submitted by email and must include identification and contact details, together with the following documents: (1) Curriculum Vitae; (2) cover
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employing advanced theoretical models and sophisticated experimental methods. Key properties of atomic and molecular anions and their interactions with other particles will be investigated to fully describe
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behavior of these components will be developed based on Finite Element Methods (FEM) complemented by Machine Learning models. Legislation and Regulations: Statute of Scientific Research Fellow, approved by
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reports about new assay development and continuing data collection and interpretation; participate in presentations to peers and supervisor both at formal lab meetings and informal meetings with colleagues
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on https://diariodarepublica.pt/dr/detalhe/regulamento/950-2019-127238533 Workplace: The work will developed at IDMEC - Institute of Mechanical Engineering, being both the Host and Contracting Entity, and
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methods: The selection methods to be used will be the following: curriculum vitae assessment and experience in modifying natural compounds and experience in cell culture for the development of biomaterials
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physiology and biochemistry. - We will value positively any knowledge or previous experience in laboratory analyses for the quantification of plant metabolites, plant phenotyping methods or biostatistics/data
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with experimental techniques including current meters, pressure sensors, and GPS surveying. Proficiency in computational methods include large-scale and high resolution hydrodynamic models. Experience
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for the amount corresponding to the Voluntary Social Security, in case of adherence to this plan.3 11. Selection methods: The selection methods to be used will be the following: Documentary evaluation (AC), with
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 16 days ago
of numerical simulations and digital twins. By using advanced machine learning methods, such as Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (vPINNs), the project