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for an excellent young life science or computational researcher to become Group Leader. Fellowships are targeted towards applicants to start their first independent group within a few years of their PhD. We offer
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these datasets to detect chromosomal abnormalities and study their breakpoints. Using statistical methods and machine learning, we will explore how these structural variants arise and which recurring structures
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. The successful candidate will work on cutting-edge projects involving artificial intelligence (AI) and computational pathology, with a particular focus on developing and applying machine learning algorithms
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data
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psychiatry. The projects will involve advanced epidemiology, pharmacoepidemiology, and machine learning methods. You will be part of a well-funded and successful research group, collaborating with
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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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into consideration. We are seeking applicants who have a PhD degree in machine learning, statistics, scientific computing, computational chemistry, applied mathematics, or a related area that is considered relevant
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, computer science, or similar topics. Experience with optimization, data-driven or machine-learning skills are meritorious. The candidate must have the PhD degree in hand before enrollment, but it is not required
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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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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