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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. This postdoc position will utilize
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the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. The postdoc will be part of the Microbial Metagenomics group
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Are you interested in real-time distributed systems, IoT connectivity, and AI-driven automation? The Department of Electrical and Computer Engineering at Aarhus University invites applications for a
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: Operator Algebras, Machine Learning, Analytic Number Theory, Automorphic Forms and Representation Theory Appl Deadline: 2025/10/10 11:59PM (posted 2025/09/10, listed until 2025/10/10) Position Description
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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competencies The applicant must hold a master’s degree in engineering and a PhD in a relevant field, such as electrical engineering, with expertise in physics-based modeling, machine learning, and optimization
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predictive framework linking genomic data to extinction risk, working at the interface of evolutionary genomics, simulation modelling, and machine learning. By integrating forward-in-time simulations, real
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thesis project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human