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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 8 days ago
testing: is a new medical treatment better than a placebo? Is a new algorithm more efficient on a computational task than the current state of the art? Due to random fluctuations, experiments must be
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/software testbed. Integrate SDRs (e.g., USRPs, RFSoC) and RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal processing algorithms for joint
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algorithms for the robot and its pneumatic actuators, enabling actuation through a reduced number of control signals and fluidic supply channels. - Performance evaluation and optimization Experimental
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algorithms that combine Reinforcement Learning techniques like Partially Observable Markov Decision Processes (POMDPs) with cognitive inference modules capable of modelling human beliefs, intentions, and goals
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assurance/insurance frameworks, including algorithmic insurance contracts, parametric insurance structures, and data-driven risk quantification methods (e.g., conformal prediction and inverse conformal
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patients with eye infections, specifically corneal ulcers, through deep learning image analysis algorithms applied in clinics and health-system interventions. The goals will be for the student to get
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will be responsible for: Implementing state-of-the-art fire spread algorithms in the fire simulator. Designing and implementing data input APIs to feed the fire simulator. Collaborating closely with
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. Skills: Analytical Thinking, Artificial Intelligence Algorithms, C++ Programming Language, Communication, Data Analysis, Feature Engineering, Git, Independent Problem Solving, JavaScript, Linux
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from astrometry, flat-field determinations, PSF uncertainties, and gain drifts. The applicant will also develop algorithms for quantifying best-fit flux and errors with outlier rejection in the all-sky
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of precipitation and temperature over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation