18 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" Postdoctoral positions at University of Lund
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The position is placed at the Division of Astrophysics in the Department of Physics to work with David Hobbs on developing algorithms to improve the quality and scientific information that can be
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around 15 are PhD students. The work environment is open and welcoming, striving to provide each employee with the opportunity to develop personally and professionally. The field of solid mechanics relates
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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Description of the workplace Our division in the Enoch Thulin laboratory develops laser spectroscopic diagnostics techniques for industrial and environmental applications. In the biophotonic sensing
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develop the techniques and instrumentation, which are used by researchers from magnetism and chemistry to biomedical and environmental science. For information on the beamline, see: SoftiMAX Description
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to physical conditions and biological activity, using video recordings from both reef sites and comparable control areas. Within this position as post-doc you will be responsible for developing tools
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computational simulation techniques are used. The research is applied to understand orthopaedic problems and to develop better methods to improve tissues regeneration. The group encompasses about 15 scientists
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methodology development for studies of size distributions, structure, kinetics and thermodynamics of protein self- versus co-assembly with a focus on co-aggregates of amyloid proteins and chaperones. Part of
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forces on microscopic objects in microchannels. We are also developing applications in medical technology. Examples of such applications include sorting circulating tumor cells from the blood of cancer
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results into practical applications for end users. Subject Description The research aims to develop machine learning models for microbe detection, focusing on the mathematical foundations in geometry and