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around understanding how species interactions change over time and space, with a focus on butterfly caterpillar-plant interactions and development of phylogenetic methods. The EvonetsLab is supported by a
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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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delimitation in biodiversity conservation using modern artificial intelligence (AI) methodologies. Based on morphological traits, traditional species delimitation methods struggle with groups like bacteria and
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cell cultures on chip. The purpose is to develop in vitro models of solid tumors that can be used to study the tumor microenvironment, and also to investigate whether acoustofluidic methods can be used
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of the leading units in the area in Sweden with particular strengths in nutritional and computational metabolomics, dietary biomarkers, micronutrient metal nutrition, nutritional immunology, marine food science
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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Uppsala University, Department of Medical Sciences Are you interested cutting-edge genomics technologies and eager to contribute to developing methods at the forefront of life science? We
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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, a national hub for life-science excellence in Sweden with cutting-edge laboratories and a vibrant multidisciplinary environment. The student will develop novel imaging methods tailored to live cell
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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