148 algorithms-phd-"INSAIT---The-Institute-for-Computer-Science" Postdoctoral positions in Spain
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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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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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Researcher (R2) Positions PhD Positions Country Spain Application Deadline 8 Dec 2025 - 23:00 (Europe/Madrid) Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Offer Starting Date 7 Jan 2026
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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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7 Nov 2025 Job Information Organisation/Company Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH Research Field Physics » Optics Researcher Profile Recognised Researcher (R2) Positions PhD
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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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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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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