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use the data to train deep learning models of cancer. This allows us to identify systems-level mechanisms that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. We
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well as Hi-C and transcriptome sequencing. You will use these datasets to detect chromosomal abnormalities and study their breakpoints. Using statistical methods and machine learning, we will explore how
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of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and
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-constrained models. Currently, we are advancing the development of single-cell models, machine learning approaches based on cultivation data, and the integration of metabolic models with computational fluid
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Magnetic Resonance (NMR), and other biophysical techniques, to investigate the molecular mechanism of RNA function. When function of these molecular machines becomes apparent, it also provides a variety of
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parental leave, sick leave or military service. The following experience will strengthen your application: Experimental atomic physics Optics Photonics Optomechanics Nanofabrication Nanomechanics Cryogenics
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of the work will be based on the data reduction framework scipp (https://scipp.github.io ). Requirements Applicants must hold a PhD in physics, materials, computational science, or a similar area of science and
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and behavioral measures, and on social cognitive decisions about the attention of others. In one of the projects, advanced behavioral methods, machine learning and eye tracking (simultaneously in two