20 postdoc-density-functional-theory Postdoctoral positions at KTH Royal Institute of Technology
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seeks an ambitious, motivated, structured, and collaborative postdoc to join the nuclear fuel materials team to study the thermal stability and pellet-cladding mechanical and chemical interaction
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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bioprocessing, as well as the development of high-throughput and pilot-scale production systems. We are now seeking a highly motivated Postdoc to drive a project focused on glycoengineering of antibodies
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to explain the role of neuron and network properties in shaping the activity. In addition, we want to understand how non-Poissonian nature of neural activity affects stimulus representation in the brain
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the development of hyperspectral 3D electric field imaging techniques in the THz spectral range, utilizing ultrashort lasers and nonlinear optical methods. The work will be conducted at KTH Laser Lab research
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work in a project focusing on crystallization of calcium carbonate in the presence of nano- and microplastics that exist in the Baltic Sea environment. Calcium carbonate is an important component of
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well as end-use requirements and sustainability. This project aims to create molecular understanding of adhesion mechanisms in bio-based wood adhesives. Anyone applying for this postdoc position should have
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description We are looking for a motivated postdoc to join our
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Description Job description The postdoctoral researcher will be part of the EU project “Digitising Smell: From Natural Statistics of Olfactory Perceptual Space to Digital Transmission of Odors.” The project
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processing. Your Role You will work on developing large scale models for sign-language processing, based on unsupervised representation learning and large language models. The models will be fine-tuned and