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Field
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given to candidates with demonstrated knowledge in the following areas: Metasurface inverse design Femtosecond lasers Candidates without a clear experience in inverse design techniques for metasurfaces
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methods: a) motivation letter (20%), b) curriculum vitae (40%), and c) experience in the area of research/previous knowledge of concepts and technologies relevant to the execution of the project (40
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criteria: - Criterion 1: Absolute merit of curriculum vitae - Criterion 2: Academic background - Criterion 3: Specific knowledge related to the work plan Knowledge in the synthesis and characterization
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the Health field is required. The following will be factors of preference in the evaluation of candidates: Having experience in research projects in the health field; Solid knowledge of quantitative and
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, and smart grids. This high level of knowledge transfer is achieved through both competitive research projects and direct contracted research. Therefore, public and private entities have access to a pool
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, an interview may be conducted with the top two candidates, where the candidates' motivation, availability, and knowledge to develop the proposed work plan will be evaluated with a weight of 30%, with
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selected for the Interview. Interview – INT (20%): Criterion E1 - Motivation, with a weighting of 30%; Criterion E2 - Technical knowledge required to carry out the work plan, with a weighting of 30
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selected for the Interview. Interview – INT (20%): Criterion E1 - Motivation, with a weighting of 30%; Criterion E2 - Technical knowledge required to carry out the work plan, with a weighting of 30
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areas*; 2. Technical Skills - Experience in processing 3D point clouds (LiDAR or photogrammetry); Knowledge of GIS, remote sensing, or spatial analysis; 3. Research Experience (Preferred) — Previous
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan