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(AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians working collaboratively. Our focus is on developing practical methods that blend traditional disciplines
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of Technology to pioneer the next generation of biorefineries using marine and terrestrial biomass. This postdoctoral position offers the opportunity to develop innovative green processes for transforming
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electromagnetic processing. The work involves close cooperation across several departments. You will have access to a well-equipped experimental environment and be part of a collaborative team dedicated to creating
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motivated colleague who will develop new environmentally benign processes for the synthesis of conjugated polymers. About the research project The Müller Research Group specializes in the materials science of
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great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part
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treat them. The Institute is composed of the following four departments: The Department of Infectious Diseases The Department of Microbiology and Immunology The Department of Medical Biochemistry and Cell
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; significant practical experience in 3D image analysis or computer vision; knowledge and experience in scientific programming (python (preferred), Matlab or other relevant language) with application to image
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of bioadhesives to better predict and optimise the production process and product properties, and thus ultimately expects to enhance the efficiency of the production process in different wood and fibre-based
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to improve our understanding of fundamental processes relevant to combustion engines, gas turbines, and fire safety. The successful candidate will join a dynamic and diverse research group with an extensive
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on close collaboration between the university and industry and aims to optimize processes, reduce error margins and increase productivity in the industrial companies involved in the project. Virtual models