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exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep learning models, which find application in image-based science, including in
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functional genomics (CRISPRi/a, Perturb seq, combinatorial screens), single cell and spatial omics, metabolomics, and immunopeptidomics. The successful candidate will pioneer assay-algorithm co design
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. The ideal candidate will provide expert instruction on neural network architectures, backpropagation, activation functions, and optimization algorithms, ensuring students gain practical skills using
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/classification, document indexing and ranking, and word and document embedding methods, while gaining hands-on experience with basic algorithms and tools used in NLP and IR. The course also explores practical
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cohorts and data generation resources through highly collaborative clinical faculty. Ideal candidates will have expertise in computational modeling, machine learning, or algorithm development, with
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of Excellence for Data Driven Discovery. Job Responsibilities: Analyze biomedical data with minimal supervision by performing advanced analysis, algorithm implementation, programming, and quality check. Assist
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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also
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include but are not limited to: · Computational / algorithmic genomics · Algorithms and data structures for statistical inference over biological data · High/super
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(Numerical Analysis and Applied Mathematics) develops numerical algorithms and software for large-scale problems in science and engineering. Its research ranges from algorithm design and analysis to software
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systems, with a focus on 3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation