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biomass characterization, biomaterial characterization and application. Experience in glycomic analysis is highly recommended. Laboratory experience in biomass transformation in materials and water based
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develop analysis pipelines to analyze high dimensional spatial and single-cell data of cancer and immune tissue from patients and pre-clinical studies and should have a strong background in both
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, measuring of growth and wood properties at the laboratory, and statistical analysis and programming. Previous experiences in forest related subjects are desirable. Fluent English language, both in writing and
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or other forms of appointment/assignment relevant to the subject area. Required skills: Strong background in aquatic ecosystem science Proficiency in GIS and analysis of long-term environmental data
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, carrying out qualitative case study research, and developing AI literacy materials (e.g., covering topics of AI risks, bias, fairness and ethical aspects, and suitable methods and tools for risk analysis
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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collaboration with the group led by Asst. Prof. Gina Panopoulou. This includes code development, analysis and interpretation of data, writing manuscripts for publication and presenting work at relevant
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microorganisms, and developing of spectral collection and analysis protocols that will allow this biochemical data to be effectively used to support optical microscopy-based deep-learning algorithms for species
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narrative/storytelling methods, intersectional analysis, and participatory research, and committed to collaborative engagement with both academic and non-academic stakeholders. The role requires theoretical
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sampling and measurement approach for improved precision in soil carbon monitoring”. Important parts of the work are fieldwork, laboratory analysis, data evaluation and academic publishing. The employed post