19 associate-professor-computer "https:" "https:" "https:" "https:" Postdoctoral positions at University of Lund
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links between dynamics, catalysis and function in protein tyrosine phosphatases, using the tools of computational biophysics. Our research group is highly interdisciplinary, using everything from quantum
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Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales. The Inverse Modelling group at the Department
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research area MERGE (https://www.merge.lu.se ), focused on climate modelling. Aerosol research has been conducted at Lund since the 1970s and is now a designated profile area at LTH (https://www.lth.se
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position, collaborating closely with the project team throughout all stages of data collection and analysis. The position will be supervised by Emma Johansson, Associate Professor at the Lund University
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Studies (LUCSUS), in collaboration with Elina Andersson, Associate Professor at LUCSUS. Position Instructions Research Focus (90%) The postdoctoral position will study the provision of grain seeds, with a
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-2024-COG no. 101171587) and is led by the Principal Investigator Yafa Shanneik, Professor of Islam and Society at SOAS , University of London. The project examines how state-led genetic healthcare
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group – lead by professor Sophia Zackrisson – with a main research interest in innovative imaging modalities and methods in breast cancer diagnostics, focusing on screening and the role of Artificial
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strategies. The research group focuses on exploration of tumor immune microenvironments through spatial omics and imaging, development of computational models for prediction of molecular and clinical features
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foundations of society in the wake of today’s divergent mobilizations. The project is led by Professor Mia Liinason. Read more about the project: Gender Struggles in the New Conjuncture. We are now looking for
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on: Computational Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images