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of rural development with a focus on energy transitions. They will be involved in empirical work linked to WP6 tasks, and develop mixed-methods grounded in qualitative research and comparative analysis
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used in synergy with immunotherapy to achieve optimal immunity against lymphomas. The project also aims to uncover the mechanisms that drive resistance to immunotherapy, using methods such as spectral
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collaborative research environment with high scientific standards. About the research project This PhD research project is focused on developing theoretical and computational methods for analyzing strong force
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own research question within this area. The project is mainly intended for quantitative methods but is to some extent also open to qualitative methods. As a doctoral student, you will read doctoral
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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methods to unravel the gene regulatory mechanisms underlying fundamental biological processes. We aim to understand how these processes are disrupted in various diseases. Our extensive collaborations with
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area “statistics to serve society” which focuses on developing statistical methods and software for the analysis of large and complex collections of data. The PhD student(s) is planned to be connected
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methods that have become known as DNA Microcsopy. The principle is that by letting barcoded strands of DNA (for example cDNA) interact and concatenate in-situ, it is possible to generate adjacency
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university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
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In this WASP financed project, the research will focus on the study of multiagent automatic control methods for closed loop (CL) control of dynamical systems that adhere to safety constraints while