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for one to two PhD students in analytical chemistry to develop analytical methods for single cell analysis and mass spectrometry imaging using direct infusion mass spectrometry. The PhD candidate will work
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methods for single cell analysis and mass spectrometry imaging using direct infusion mass spectrometry. The PhD candidate will work with and develop custom made techniques coupled to high resolving mass
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environmental pathogen detection and who has experience in wastewater analysis. About the position You will be part of the Swedish Environmental Epidemiology Center (SEEC) within the Division of Microbial Ecology
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ecology, and/or restoration ecology. Experience in design, execution and analysis of acoustic data is desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS
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long-term experiments. Your profile The candidate must have a PhD degree in silviculture and/or forest management or a very similar subject. The candidate must have proven experience in data analysis and
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and/or dynamic approaches to detect them in the code or prevent their execution at runtime. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms
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logical coherence in the formulation of the aim and the research questions stringency of legal reasoning and analysis adequate selection of methods and theory capacity for creativity and innovation in
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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
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on: Technical Expertise: Documented skills in Python, Matlab, R, and a strong working knowledge of UNIX environments. Proven familiarity with biological omics data analysis techniques is essential, along with any
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo