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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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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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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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graduation. An excellent academic record, a deep understanding of immunology/immunotherapy (human/mouse) as well as practical experience of immune cell biology, cell culture, gene edition (crispr/cas), flow
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research team. The following are considered as advantage: Any previous experience in working with spacecraft data. Deep knowledge of AI methods (esp. machine learning based) and their performance estimation
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separation technologies from different points of view. The work requires deep scientific understanding of the related physical and chemical phenomena, exploration and development of novel and existing
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deep learning frameworks What We Offer: Innovative Projects: Work on high-impact health research and decades of extensive real-word health data using the latest analytic and AI methods in
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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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-of-the-art research on particle-related separation technologies from different points of view. The work requires deep scientific understanding of the related physical and chemical phenomena, exploration and