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systems. The successful candidate should have a strong interest in mechanistic biology and in using experimental approaches to understand fundamental questions in cancer development. Computational
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are essential, along with the ability to build and extend statistical pipelines. An interest in Bayesian inference applied to biology is also important. A background in computational proteomics or LC-MS/MS
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(DoSChem) – Austria’s largest doctoral training program in chemistry. DoSChem brings together more than 200 doctoral students and over 50 PIs, fostering interdisciplinary and curiosity-driven research
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This 3.5-year PhD project is fully funded by The Department of Mechanical and Aerospace Engineering; students who are eligible to pay tuition fees at the Home rate are eligible to apply
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are excited to work across disciplines and contribute to cutting-edge research at the interface of biology, chemistry, imaging, computation, and AI. Molecular Perturbations: Chemistry Engineering Biology
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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sequencing, flow cytometry, multiplex immunofluorescence, spatial transcriptomics, and standard molecular biology approaches. A computational component may also be available, depending on the skills and
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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or Python). Knowledge of the social identity approach and health psychology models are essential, and experience of network science or computational modelling is desirable.
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catalysis. Computational chemistry allows the design and discovery of new molecules and theoretical understanding. This is especially important when studying the behaviour of sensitive chemicals, like