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- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
- Universitat de Barcelona
- CRAG-Centre de Recerca Agrigenòmica
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working in the development of gene therapy strategies for neurological paediatric rare diseases using AAV vectors, animal models and iPSC-derived neurons from patients, with the aim to arrive to clinical
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candidate will contribute to the theoretical and physical chemistry aspects of the project, with particular emphasis on the analysis and modelling of non-equilibrium chemical systems and reaction–diffusion
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optimize gene expression systems. Perform phenotypic and molecular characterization of engineered strains. Prepare scientific manuscripts, patents, and project proposals. Collaborate closely with
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Education and qualifications: Required: PhD degree in Biotechnology or related fields. Experience in experimental models and molecular microbiology of serious infections (e.g., bacteraemia, endocarditis
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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codes is essential. Experience with modelling conjugated organic molecular-based systems (e.g. electronic structure, charge transport) would be particularly highly valued. Experience in coding with Python
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. The successful candidate will be joining the Molecular Nanophotonics group led by Prof. Dr. Niek van Hulst. Key responsibilities Contribute to Project FastTrack, to track energy (exciton/charge) transport in
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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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on breakthroughs in leukemia treatment in the elderly and understanding the impact of viruses on genetic mutations leading to cancer. “This new model could help cultivate a new generation of medical professionals
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research communities. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques