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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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high-throughput stimulus-response experiments and 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
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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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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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science skills (e.g., machine learning). Prior experience in professional analysis of register data will be a plus. Applicants should be highly collegial and experienced in working effectively in “team
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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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complexity persist. With the development of optimized array designs and AI-driven methodologies, this research aims to explore the principles of array signal processing and machine learning to enable the next
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prediction and personalized treatment in child and adolescent psychiatry. The projects will involve advanced epidemiology, pharmacoepidemiology, and machine learning methods. You will be part of a well-funded
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such as epidemiology, biostatistics, computer science, statistics, etc. We will also consider those with PhDs in other areas but who have advanced/relevant data science skills (e.g., machine learning
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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