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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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with both academic researchers and industry experts across Europe. Your key responsibilities will include: Conduct analysis of polymer membrane waste materials Develop electrospinning methods to produce
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, space, modal content, etc. However a systematic analysis of the potential and trade-offs of applying photonic parallelism to computing is still lacking. This PhD project aims to get an in-depth
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provides several degrees of freedom for data encoding and processing, time, frequency, space, modal content, etc. However a systematic analysis of the potential and trade-offs of applying photonic
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. within energy in buildings and/or built environment. Preferably documented qualifications within working with data, data analysis, modelling energy and indoor environment, building control as
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: Hands-on experience with proteomics workflows and mass spectrometry-based data analysis. Basic knowledge of protein biochemistry and enzymology. Familiarity with machine learning frameworks (e.g., PyTorch
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federated knowledge graph framework that facilitates the querying, consolidation, analysis, and interpretation of distributed proteomics-focused clinical knowledge graphs. To achieve this, we will employ
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the research group Bioactives Analysis and Applications, and as part of the prestigious Alliance PhD program, you will collaborate with the Technical University of Munich (Germany). If you're ready to
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understanding of environmental assessment and are comfortable working in a laboratory environment. A background in chemical risk assessment (including exposure analysis, analytical chemistry, ecotoxicology, and
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are comfortable working in a laboratory environment. A background in chemical risk assessment (including exposure analysis, analytical chemistry, ecotoxicology, and/or human health aspects) will be seen