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causal relations between genetic alterations and disease phenotypes. In this PhD you will address this by a deep learning model of drug responses in cancer. The PhD position focuses on predicting cell type
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phenotypes. In this PhD you will address this by a deep learning model of drug responses in cancer. The PhD position focuses on predicting cell type-specific drug responses, identifying transcriptional
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proteomics methods in a clinical setting. The PhD student will be part of a multi-disciplinary team consisting of experts in cancer biology, proteomics, bioinformatics as well as lung oncology. Within
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-learn) and topological data analysis is also considered a plus. Proficiency in oral and written English is necessary. As the PhD student will be part of a multi-disciplinary team of experts in cancer
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Automated Quantitative Assessment of Tumor Burden and Lesion-Wise Analysis in Metastatic Cancer We believe that you found your way here because of your deep interest in analysis of complex imaging
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biostatistics across a broad range of areas within biomedical science. The department is among the largest of its type in Europe and has especially strong research profiles in psychiatric, cancer, reproductive
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, including around 45 PhD students, work at the department. New employees and students are recruited from all over the world and English is the main working language. The department is located at the Biomedical
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analysis using artificial intelligence. The research project is centered around predicting drug response at the single cell level for children with cancer, using publicly available data augmented by new data
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to develop computational algorithms and methods that use omics data to infer gene regulatory networks (GRNs), and apply these to understand regulatory mechanisms that lead to cancer formation. Cancer
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in translational medicine and molecular biosciences. Project description This PhD student position is available in the laboratory headed by Associate Professor Marc Friedländer – part of the MBW