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. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3 administrative
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teaching load of 2 classes per year. The position is for two years, and can be renewed for a third year pending satisfactory performance. Required Qualifications ● A PhD in Mathematics or an equivalent area
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the complexation process. In parallel, these systems will be evaluated as organocatalysts, with the ultimate goal of achieving highly enantioselective transformations. Candidates should hold a PhD in chemistry and
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opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel
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into hours, scale evidence review across thousands of studies, and explore many hypotheses in parallel so researchers and clinicians can focus on strategic decisions and patient care. Every solution you build
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on estimated movements using eDCCs. The research will focus on data simulated using the Monte Carlo method and real data from clinical SPECT scanners with a parallel collimator, such as those available at LUMEN
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, which also encompasses single-cell resolution spatial transcriptomics of human plaques to precisely identify and characterize disease-relevant smooth muscle cell phenotypes, a parallel track of organoid
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21 Aug 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Portugal
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this project, we will develop neural diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also
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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance