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to molecular mechanism. New experimental and computational methods, including data and deep-learning driven approaches to study complex biological processes in the context of cells, organisms, communities and
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, Genome editing tools, Regulatory mechanisms, Synthetic genomics, Genotype-to-phenotype & genomic-environment interactions, Metabolism, Single cell and spatial omics development Deep learning-enabled
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The Machine Learning for Health team in the Data Science and Genetic Epidemiology Lab at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a highly
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environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch and TensorFlow is highly desirable Familiarity with multimodal data fusion/integration is highly
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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-scalar integration, or AI applied to biomedicine. Expert knowledge of causal analysis methods, biological network modelling, machine learning, and deep learning. Experience in collaborative environments
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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for statistical computing and data visualization Deep learning frameworks, such as PyTorch or Tensorflow and data science tools such as Numpy, Pandas and Matplotlib Experience in machine learning management systems
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for statistical computing and data visualization Deep learning frameworks, such as PyTorch or Tensorflow and data science tools such as Numpy, Pandas and Matplotlib Experience in machine learning management systems