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implementing algorithms based on online Sparse Gaussian Processes and advanced probabilistic techniques enabling AUVs to dynamically alter their trajectories, cutting down on uncertainty and improving efficiency
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asap, focused on ‘Quantum Machine Learning’, with the objective of investigating hybrid classical-quantum and quantum inspired algorithms. The tasks will include the design and implementation
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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to facilitate perceptual learning of different stimulation patterns; and (iii) the development of advanced AI algorithms capable of converting camera input into real-time electrical stimulation parameters. In
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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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Experience: In the development of medical devices in vision sciences. Implementation of psychophysical algorithms for vision. Design and analysis of clinical studies. Experience in functions similar to those
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Infrastructure? No Offer Description Research line: Learning in single cells through dynamical internal representations. Job description: Develop theory, models and algorithms for identifying molecular encodings
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of auditory stimuli and creation of stimulation sequences 5) Implementation of pilot studies in adults and infants 6) Programming analysis algorithms for FFR, MMN and statistical learning, based on spectral and
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: Own the optimiser: design, implement, and tune heuristic/metaheuristic algorithms (e.g
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group