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battery-related hydrometallurgical leaching as a model system, the project combines controlled experiments, real-time monitoring, chemical analysis, and data-driven modelling. The postdoctoral researcher
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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strong scientific background with relevant expertise in cell and/or molecular biology. Interest in programming, computational biology and statistic towards high-throughput data analysis is considered a
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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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for this position have a great interest and knowledge in data analysis, filtering and modeling based on large amounts of data. It is important that you have a solid understanding of mining and mining systems to also
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for experimental data analysis Assessment criteria This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of the applicants
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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postdocs Drive quality assurance work and documentation and presenting data, results and method development internally and externally, both in written and oral presentations Supervise master’s and/or PhD
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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geoinformatics and spatial data science, including demonstrated knowledge of GIS, spatial analysis, spatial modelling, and GeoAI. Experience and expertise in WebGIS development, familiar with the core technologies