50 data-"https:"-"https:"-"Institute-for-Basic-Science" Postdoctoral positions in Denmark
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). Personality disorder (PD) characteristics are often present in patients with eating disorders (EDs), but we know little about the epidemiology or biological underpinnings of comorbid ED and PD. Using data from
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scenarios for new production concepts at an early stage, develop models and simulations for these scenarios, and verify relevant data at plant-simulation level. A central part of the work will be
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to experimentally derived disorder models and data from methods such as total scattering/pair distribution function analysis, diffuse scattering, diffraction, solid-state NMR and electron microscopy. Work closely
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postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
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analyze interviews with company representatives, trade unions, labour inspectors and other authorities, and experts. Gather and analyze supplementary data such as statistics, archival data, media sources
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benchmark the developed framework using project data, high-fidelity simulations including hardware in the loop, and relevant industrial case studies, assessing its robustness and computational performance
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machine learning to rich molecular data and uncover the molecular basis of IBD. The successful candidate will build prediction models from high-dimensional molecular measurements, with a particular focus on
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find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here The appointment process at Aalborg University involves a shortlisting process. You can
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employment The place of work is Universitetsbyen 81, 8000 Aarhus C, Denmark, and the area of employment is Aarhus University with related departments. Contact information For further information, please
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methods for approaches such as model-data fusion techniques. The main focus of your position will be to develop and apply a scalable process-based modeling framework to evaluate climate-smart management