42 data-"https:" "https:" "https:" "https:" "AALTO UNIVERSITY" PhD positions at Utrecht University
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group boundaries. Data : Leveraging existing datasets (e.g., Statistics Netherlands [CBS] administrative microdata on employees linked to organizational surveys) and collecting original data (e.g
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philosophical approaches; Learn to derive hypotheses from deductive theoretical reasoning through formal methods on sociological phenomena; Develop strong skills in experimental methods, data analysis and
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PhD Position on Parameterized and Fine-Grained Complexity of NP-Hard Problems Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40
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3D printers, furnaces, centrifuges, and microfluidic devices. Meticulous data recording and analysis are essential, as the project combines practical engineering with fundamental physical chemistry
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existing datasets and set up new studies, collecting samples from horses and performing laboratory experiments followed by data analysis. Techniques such as 16S rRNA sequencing, shotgun metagenomic
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quantitative data and qualitative fieldwork. Publishing research findings in peer-reviewed academic journals and presenting them at conferences. Collaborating with interdisciplinary researchers and engaging with
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surveys and field experiments Ability to organise data collection Proactive in engagement with stakeholders and local communities, and setting up collaborations and on-site research activities We look for
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-throughput phenotyping. You will develop novel methodologies, execute experiments, analyse data, and present your project results in the form of manuscripts and oral presentations. In addition to your research
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, or similar) for data analysis and modeling; Theoretical background and genuine interest in porous media processes, such as fluid flow, reactive transport, soil-fluid interactions, or geomechanics; Excellent
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering