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. The researcher will develop novel research that applies advanced data science, machine learning and deep learning to various different data modalities. An ambition of this team is to implement predictive modelling
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work on research projects employing latent variable modeling and risk prediction methods to better understand substance use related morbidity and mortality outcomes (e.g., overdose, hospitalization
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Position Information Position Information Working Title Manager, Biophysical Model Design (Temporary) Department Biochemistry-0831 Requisition Number S_260080 Posting Open Date 02/04/2026
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economics and finance. Leveraging its ‘4-in-1’ model of education and residential college system, UM provides all-round undergraduate education, nurturing talent to support social and economic development in
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new vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from
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their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in the field of genomics, evolution, population
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the changing climate. The appointee will work in the research team supervised by the Associate Director of Research, on projects that include the prediction of flooding in coastal areas, wave runup and coastal
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intelligent surfaces Main supervisor: Prof. Viktar Asadchy[AALTO] Co-supervisors/mentors: Dr. Victoria Tormo [INDRA] and Dr. Barthès [3DEUS] Objectives To establish an analytical modeling approach
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The