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) group at Aalborg University. The position is for two years and combines theoretical research, algorithm development, simulations, embedded implementation, laboratory experimentation, and industrial
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one day a week on campus. Job description The computational biologist will be expected to: Lead the development of acoustic detection and classification algorithms for marine mammals. Evaluate algorithm
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thereafter. We are seeking a highly motivated quantum algorithms researcher to develop advanced computational methods for solving partial and ordinary differential equations (PDEs and ODEs) with applications
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in e.g. qubit measurements and quantum error correction algorithms. The core responsibilities include modeling, simulating and designing components and chips for photonic and neutral atom quantum
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interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and quantum algorithms that solve life
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, outreach and knowledge brokering activities of the Algorithms, Data and Democracy project (ADD) of which the IPA project is part (see www.algoritmer.org ). teach, supervise and examine students in Master of
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spectrum of topics, ranging from fundamental vision algorithms and deep learning methods to applied research projects carried out in close collaboration with industrial and public partners. Read more on
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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
: * Development of advanced signal processing methodologies for transient electromagnetic data * Development of transient electromagnetic forward modelling and inversion algorithms. * Development of processing
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closely related field, with a strong background in AI/ML technologies, including algorithm development, optimization, and data-driven modeling. Candidates are expected to demonstrate research leadership
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, these advanced compression techniques allow for on-the-fly data compression, support analytics and machine learning (ML) without decompression, significantly reduce algorithm complexity and memory use