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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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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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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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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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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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, 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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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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, cancer, reproductive, pediatric, pharmaco, genetic, and geriatric epidemiology, eating disorders, precision medicine, and biostatistics. Part of the success of our department is due to our collaborative
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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
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to cancer is carried out. Approximately 300 people from over 40 nations are currently working at the department. About 30 research groups with various cancer research profiles are involved and we have around