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areas of nanoscience and nanotechnology. Job Title: Junior Postdoc-Electrocatalysis; in situ electron microscopy characterization; deep learning frameworks for TEM image analysis Department: Advanced
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with deep learning to design van der Waals heterostructures with optimised spin-orbit torque (SOT) efficiency for ultra-low-power memory and computing. Its two pillars are AUTOMATA, an automatic material
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, engineering or physics. Knowledge: Computational programming, machine learning, quantum transprot, device simulation. Professional Experience: use of device simulation codes applied to 2D materials. Personal
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research environment. Personal Competences: We are looking for a proactive, organized, and collaborative person, with a genuine interest in people and strong motivation to learn and grow. Summary
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mandatory. · Personal Competences: We are seeking highly motivated, independent thinkers, who are well organised and willing to learn. Summary of conditions: Full time work (37,5h/week) Contract
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micro/nanofabrication techniques. 5) Contribute to the development of experimental instrumentation, automation routines, and laboratory infrastructure. 6) Acquire, organize, curate, and maintain
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
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. Knowledge and Professional Experience: Interest of learning (S)TEM, EM related spectroscopies and in-situ techniques (use of gas and/or liquid, bias and heating STEM sample holders). Previous experience
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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible