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Star Formation. CARINA is focussed on developing case agnostic, multi-sector machine learning tools to efficiently handle datasets generated by integral field spectroscopy and time domain surveys
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agnostic, multi-sector machine learning tools to efficiently handle datasets generated by integral field spectroscopy and time domain surveys. The testbed for the machine learning tools is the question
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Forefront Astronomical Instrumentation to Probe Intermediate Mass Star Formation. CARINA is focussed on developing case agnostic, multi-sector machine learning tools to efficiently handle datasets generated
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development of decision support tools https://www.upv.es/entidades/SRH/conypi/A1272498.pdf Where to apply Website https://sede.upv.es/oficina_tactica/?idioma=en#/inicio Requirements Research FieldEngineering
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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in machine learning theory and practice: you understand not just how to use tools, but how they work Deep learning expertise with modern frameworks (PyTorch, TensorFlow, JAX) Experience developing
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their skills. This Associate in Research will become a member of a multidisciplinary team that is utilizing chemical tools and biological methods to uncover novel aspects of malaria parasite biology with
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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3 Mar 2026 Job Information Organisation/Company UNIVERSITE LE HAVRE NORMANDIE Research Field Computer science » Computer systems Computer science » Database management Computer science » Modelling
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating