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Field
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celebrated under the Labour Code, of 1 (one) doctoral researcher position as Assistant Researcher under private law, for the exercise of scientific research activities in the scientific area of Computer and
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subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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), of the Polytechnic of Leiria Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Application Deadline 31 Mar 2026 - 23:59 (Europe/Lisbon) Country
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subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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), of the Polytechnic of Leiria Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Undergraduate Positions Application Deadline 31 Mar 2026 - 23:59 (Europe/Lisbon
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Application deadline: 30.04.2026 Estimated date of the contest settlement: May 2026 Envisaged job starting date: tbd Period of employment: 24 months The competition is open to each candidate who meets
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computer skills from a user’s perspective; • Good verbal communication skills and fluency in Portuguese and English; • Professional rigour and good teamwork skills; • Immediate availability. 8
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing