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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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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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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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project. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power
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meetings Potentially participate in Arctic field campaigns Be working with large data sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will