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vibrational spectroscopy and microscopy techniques, the doctoral student will build a unique spectral and imaging database of Mars analogue soils and link it to spacecraft mission data (e.g., SuperCam, SHERLOC
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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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activities, including the collection, analysis and documentation of qualitative and quantitative data. The role involves preparing research reports, developing data collection tools, communicating project
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and in vitro assays. Experience in catalysis, programming and large data analysis is a plus. Excellent communication skills and fluency in English (written and oral). Our offer The project is planned
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). Online vibrational spectroscopy measurements (such as Raman and FTIR). Infrared microscopy imaging of individual microbial cells. Reference analyses of the bioprocess samples with standard analytical
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
learning (noise robustness, statistical generalisation) with those of symbolic AI (explainability and logical reasoning). The objective of this thesis is to study the contribution of neuro-symbolic
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-content microscopy and AI/deep-learning-supported image analysis will be used to quantitatively assess neuronal morphology and maturation. Parameters will include dendrite length and branching, axon growth
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to coordinate participant recruitment, OPM-MEG and eye-tracking data collection, analysis, documentation and reporting to meet deadlines. You will adapt existing and develop new scientific techniques and child
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quantitative and/or qualitative research methods and be willing to further develop expertise in advanced methodologies. Experience with survey research, content analysis, statistical software (e.g., R, SPSS
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, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning or high-performance computing is an advantage. Prior knowledge of pathology or breast