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sustainability of biomaterial manufacturing through safe design methods, machine learning, and predictive life cycle assessment as well as developing machine learning and hybrid digital modeling methods, combining
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate surrogates
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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Experience in advanced statistical analysis models Experience working with large imaging datasets Experience in advanced MRI sequence and analysis Ability to work well in a multidisciplinary team
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based on catechol molecules, present in various living organisms (e.g., mussels), have demonstrated unprecedented adhesive properties under wet conditions, biocompatibility, low toxicity and low cost
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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models and reconstruction algorithms. Develop or adapt scripts (e.g. in MATLAB, Python) to facilitate data analysis workflows. Prepare scientific reports, publications and presentations to disseminate
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frameworks, and predictive modeling. Familiarity with high-performance computing environments. We Offer: Number of available positions: 1 Starting date: December 2025 Working conditions: Full time 1.5-year
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DESCRIPTION The Chromatin Biology Laboratory, led by Alex Vaquero, is internationally recognized for its expertise in studying cellular response mechanisms under stress conditions. The group's main objective is
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and responsibilities: Synthesis of nanoparticles as drug delivery carriers In vitro and in vivo studies on the efficacy of the developed nanomotors in 2D, 3D in vitro models of different diseases. Study