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biological collection, access to specialised analytical infrastructures, and a consolidated network of collaborators, elements that ensure adequate conditions for the technical and scientific development
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parameters on part distortion: mesh size, mechanical properties, and use of supports. V - Initial grant duration: 4 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial conditions
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doctoral degree, in the scientific area of Electrical and Computer Engineering [JB2.1][JB3.1]knowledge of generative neural networks, C++ and Python programming, able to use Docker container platform, Linux
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of indirect non-target effects of the biological control agent Trichilogaster acaciaelongifoliae Analyse potential indirect effects of the biological control agent using an ecological network approach, focusing
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Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), trained on a new dataset of biological images of Candida fungi, the solution aims to improve the speed, accessibility, and accuracy