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, didactics and learning, with approximately 240 full-time researchers, including 80 PhD students, and 4,500 Bachelor’s and Master’s degree students. The school’s activities are characterised by a high degree
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at the intersection of AI, RF, and wireless communication. Your main tasks include developing machine-learning methods for wireless interference detection, mitigation, edge intelligence, and applying AI to optimize RF
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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is expected to hold a PhD degree relevant to the topics of the fellowship. Such a degree might be in (Medical) Sociology, Public Health, Epidemiology, or another area related to survey data analysis
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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. Required Qualifications: Completed PhD in the field of Biotechnology, Biochemical Engineering, Biochemistry, or relevant field Strong background in enzymology and protein engineering Experience in enzyme
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to interact and collaborate to develop robust ways to decode single molecule imaging data. Your profile The candidate should hold a PhD in biophysics, chemistry, nanoscience or related subjects and have a
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). Applicants should have completed their PhD in Finance or a related area prior to starting. We also encourage seasoned candidates with a strong research pipeline and teaching experience to apply. Job