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technologies. Conducts research on advanced space systems technologies including communication architectures, radiation-hardened electronics, spacecraft subsystems, control algorithms, and mechanical designs
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growth. Develops innovative solutions and novel approaches to space systems challenges, including new communication architectures, radiation mitigation strategies, advanced control algorithms
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
dynamic photonic sensor array with on-chip neuromorphic intelligence. This is enabled by significantly advancing the state of the art in optoelectronic algorithm-hardware co-design and multi-scale
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required. Candidates should be comfortable developing and teaching the core MADS courses offered by the Computer Science Department (CSC 501: Algorithms and Data Models; CSC 502: Systems for Massive Datasets
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and AI algorithms Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience working with geospatial data (e.g., geopandas
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development of post-graduate students. Particular attention will be given to candidates with experience in topics that are relevant to data science, most notably mathematical and algorithmic foundation
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large datasets. Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements. Use system reports and analyses
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and statistics, machine learning, information theory, optimization, and algorithms, to better understand the behavior of markets and direct our trading. We also work together to continuously enhance
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, quantum computing algorithms/architectures, applications of quantum computing; or 2. Quantum photonic integrated circuits. Candidates must hold a Ph.D. in Electrical Engineering, Computer Science, Physics
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.), Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96