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data (HRMS) used for non-target analysis. The projects aims to develop a combination of supervised and unsupervise machine learning stragaties for pinpointing chemicals that have high toxicity
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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constraints such as sensitive devices running in a medical environment could be considered. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms, vulnerability
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and reflective empirical analysis with strengths in intersectional perspectives on gender and sexuality and feminist methodologies. Read more about about our research themes here: https
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credits, or Mandatory requirement for English equivalent to English B/6 Experience in formulating lipid nanoparticles with mRNA via microfluidics Experience in sample preparation and data analysis from
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of peptide-nanocellulose hydrogels and the analysis and publication of the scientific results. The following tasks are included in the employment: Peptide design, synthesis and characterization In vitro cell
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, among other things, how the production of cement-based materials is affected by electrified processes that reduce carbon dioxide emissions, how trace elements such as rare earth metals are bound in
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the vicinity of perennial snow fields during the growing season. The PhD student will also be responsible for analysis of data from eddy covariance sensors, weather stations, and hydrological installations, as
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LevelMaster Degree or equivalent Skills/Qualifications Merits for employment are: experience and knowledge in organic chemistry, chemical analysis using spectroscopic methods, emulsion polymerisation and
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University Work assignments The work primarily includes functional testing of antibodies through experiments, analysis, and compilation of results. The role also involves cell work and the use of a wide range