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dataset, GroupLens Research. grouplens.org [8] Statlog German Credit Data, UCI Machine Learning Repository. archive.ics.uci.edu [9] ERASER Benchmark, ERASER Project. eraserbenchmark.com [10] e-SNLI Dataset
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developing statistical and machine learning approaches for the integration of cancer multi-omics data and the analysis of CRISPR-based screens. Responsibilities include designing bioinformatics workflows
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projects across the following areas: Spatial and Single-Cell Proteomics in Childhood Cancer Cell-cell communication & cellular fitness in CAR-T & CAR-NK therapy Deep learning & LLMs in mass spectrometry data
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of the Saez Rodriguez group is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, auto
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and
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two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication fields
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level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and immunological problem. Improving
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models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue samples. Requirements: PhD completed by
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess