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CCSE - Associate Professor in Computer Science - Integrated Preparatory Cycle & Engineering Programs
of affiliation is retained. TEACHING ACTIVITIES Teach computer science across the preparatory cycle and the engineering and Bachelor programs: computer-science foundations for engineers, algorithmics and data
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of the data and the different parameters used by algorithms. Subsequently, the course will introduce different qualitative and quantitative methods to assess the quality of the results. All concepts will be
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Primary Duties & Responsibilities: Assists research studies with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic
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algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing extreme-scale scientific data management
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at the interface of optimization, geometry, and machine learning Designing, implementing, and testing algorithms Engaging in scientific exchange with collaboration partners of the project Preparing reports
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processing, and software-defined radio experimentation. Depending on your background and interests, your research may emphasize theoretical modelling and algorithms, experimental implementation, or a
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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to apply. Responsibilities include: Algorithm development and/or software design Develop C/C++ or equivalent code to interface with developed hardware and associated interfaces (SPI, I2C, etc) Integration
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Synthetic Aperture Radar (SAR) data for flood events. Developing algorithms to map flood extent, and event duration. Contributing development of data-fusion methods and modeling. Assisting with proposal
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development of in silico models and generative AI algorithms for the production of high-resolution synthetic images, validated through the use of quality metrics and comparison with clinical data. The goal is