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: Two-Sample Test for High-Dimensional Covariance Matrices [RI 4/22 ZTM] Project Introduction: With the rapid development in data collection and storage techniques, vast amounts of information
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constituents present in the developed food matrices. Applicable Legislation: Law nº.40/2004, 18th August (Research Fellow Statute) amended and republished by Decreto-Lei n.º 123/2019, 28th August; FCT Regulation
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complexation and recovery. The candidate will design and implement engineered peptide- and protein-based systems for selective REE capture, explore their immobilization onto synthetic matrices. Key
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variety of conditions that are not optimal growth conditions. The survival data of pathogens under these conditions (growth matrices + sub-optimal growth temperatures + antimicrobial stresses) will also be
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discipline. A proven track record in characterisation of the structure or mechanical properties of solid-state materials. Experience with the analysis of either mechanical testing data (e.g. compression tests
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: Undertake these responsibilities in the project: i. Design and optimize NGS library preparation protocols for low-quality and low-yield DNA derived from complex fuel matrices. ii. Conduct laboratory
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: Undertake these responsibilities in the project: Design and optimize NGS library preparation protocols for low-quality and low-yield DNA derived from complex fuel matrices. Conduct laboratory experiments
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our lab. Potential applications of interest include artificial extracellular matrices for regenerative medicine, breadboards for localized molecular computing, and nanophotonic devices. Present
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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for selective REE capture, explore their immobilization onto synthetic matrices. Key Responsibilities: Apply omics-based approaches (genomics, transcriptomics, proteomics, and metabolomics) to identify and