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a motivated and experienced candidate to work on computational modelling of low dimensional electroactive materials for energy applications. The project will focus on systems based on arrays of stable
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structure, dynamics, and switching mechanisms at the nanoscale, including optical manipulation of polarization states. Design and execution of experiments demonstrating optical control of ferroelectric
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. The successful candidate will be joining the Ultracold Quantum Gases group led by Prof. Dr.Leticia Tarruell . The Fermi-Hubbard model is a cornerstone model of condensed matter physics. It describes the physics
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study for the evaluation of analytical fingerprinting techniques such as food fraud screening for the improvement of official agri-food inspection and control plans Functions and Tasks: The postdoctoral
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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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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition
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at enhancing biogas production. Development and operation of bioprocesses in bioreactors at both laboratory and pilot scale. Analysis of biological and physicochemical parameters to monitor and control microbial
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hardware constraints, improve system control, and unlock new modes of problem-solving that surpass classical approaches. The ML-QSIM project is built upon a robust multi-node and multi-regional structure
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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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colloidal routes, enabling precise control over size, morphology, composition, and structural complexity. This role offers an unparalleled opportunity to lead the computational core of a cutting-edge