26 sensor-algorithm-"Fraunhofer-Gesellschaft" Postdoctoral positions at Nature Careers
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efficient decoding algorithms" supported by the Luxembourg National Research Fund (FNR). The APSIA Group is seeking a highly qualified post-doctoral researcher for this project. For further information, you
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. The most recent methodological research of the group includes algorithms for cell type deconvolution, high-resolution purification, and integration of single cell multi-omics data. This postdoctoral fellow
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You will work closely with colleagues both
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priming and susceptibility to infections. The project aims at understanding how endogenous nucleic acids can contribute to the basal activation of innate sensors. Our group previously studied the role
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attacks Develop and implement ML algorithms to identify vulnerabilities and predict potential threats in supply chain systems Prepare project deliverables and disseminate results through high-quality
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priming and susceptibility to infections. The project aims at understanding how endogenous nucleic acids can contribute to the basal activation of innate sensors. Our group previously studied the role
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programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
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, focusing on its roles in metabolic pathways essential for biosynthesis and redox balance. Our work explores how p53 functions as both a sensor and regulator of cellular metabolism. We are also identifying
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with