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Field
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computational and machine learning approaches, you will decipher genomic regulatory programs and infer the evolutionary patterns of gene regulatory networks in cortical neurons, study their developmental origin
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guarantee for various machine learning methods; and designing fast and efficient algorithms and analyzing the convergence performance of those algorithms; co-authors scientific publications and reports
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Drs. Koenraad Van Doorslaer and Michael Kuhns. This challenging position requires a high level of enthusiasm, dedication, and drive for applicants interested in conducting evolutionary guided research
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new MRI techniques for motion-robust imaging, real-time image processing, and/or deep learning. The work includes MRI pulse sequence design for various MRI techniques and technologies, algorithm and
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in
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NSF funded projects, advancing the knowledge about distributed systems, developing novel algorithms for distributed resource and workload management, simulating and emulating systems, as
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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We
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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research