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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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, computer science, bioengineering, data science, or a closely related discipline. • Demonstrate advanced proficiency in artificial intelligence and machine learning, particularly in applications involving
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and
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Experience in machine learning techniques Postdoc 3: Experience in the computation and analysis of hydrodynamic cosmological simulations of galaxy formation and evolution Experience in simulations
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, computer scientists, and engineers. -Mentor and supervise master and PhD students. -Design, analyze experimental work, contributing to exploitation and dissemination of the project in the form of patents and
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documentation. Contribution to applications to HPC centres (with a focus on EuroHPC machines) in order to secure the resources needed for architecture-dependent code development, optimised deployment and
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• Academic record: 50 - Doctorate in the specialty 50, Generic Doctorate in science/technology 20 • Work experience: 30 - >3 years 30, <3 years 10 • Computer skills: 10 • Languages: 10 - Spanish level B1
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is to elucidate the molecular mechanisms through which somatic mutations affecting RNA splicing contribute to tumorigenesis, and to investigate the mechanisms driving resistance to CAR-T cell therapies
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Biology, Physiology and Immunology at the Faculty of Biology. Learn more about our research: https://www.ciberned.es/grupos/grupo-de-investigacion?id=28735 https://www.neurociencies.ub.edu/research-group