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Field
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mapping, and scenario analysis. The project is led by Professor Frank Dignum (main supervisor) and Dr. Jason Tucker. Requirements The general admission requirements for doctoral studies are a second- cycle
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experience in large-scale quantitative and functional proteomics Experience in sample preparation of cells for quantitative proteomics Strong skills in quantitative proteomics data analysis using R, as
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proteomics Strong skills in quantitative proteomics data analysis using R, as well as knowledge in bioinformatics Solid understanding of mitochondrial functions and cellular metabolism Experience in cell
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spectrometry or alternatively structural biology. Knowledge in data analysis and statistics. Strong ability to communicate effectively in English, both orally and in writing. Documented experience in scientific
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, power system analysis, as well as experience in modelling, simulation, or experimental work. Ability to work independently, in a structured and goal-oriented manner, both individually and in collaboration
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. Strong programming skills in R and/or Python are essential, as well as prior experience in data analysis, statistics, or machine learning. The project involves large-scale single-cell and spatial
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the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning to enable innovative interactions with unmanned aerial vehicles
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, or English, a summary of max 1,000 words must be included, outlining method, theory, and results/analysis. A CV in Swedish or English showing eligibility for PhD studies in Sociology of Law. Degree
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enrichment techniques combined with MS/MS methods and data analysis have revealed a large extent of PTMs in proteins a lack of efficient affinity-based enrichment techniques causes several of the modifications
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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve