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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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Experience in cell/molecular biology techniques or fluorescence microscopy is required; expertise in both is a strong advantage Experience with iPSC-derived cells, primary neuronal or glial cultures, or mouse
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(Technology for engagement and personalized support of people with aphasia in rehabilitation). The candidate will join a research team with extensive experience in ergonomics and HCI, particularly in the design
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communication skills and a collaborative mindset Full professional proficiency in English Good to have: Hands on experience with at least one paradigm for quantum optimization with quantum computing (QAOA
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analogues, and participate in bioconjugation experiments, as well as in the purification and characterization (mass spectrometry) of the obtained analogues and conjugates. The recruited candidate is expected
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for experiments below the MHz to detect the current fluctuations. We propose for the internship to use our newly developed ultrasensitive TMR-based (Tunnel Magnetoresistance) magnetic field sensor to detect
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 23 days ago
experience in the preparation and physico-chemical characterization of bio-based aerogels [2]. The rheological characterization and magnetic behavior of the bio-aerogel bead assemblies will be investigated
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nanorods interactions from experimental data. The ideal candidate has significant experience in computer simulations and programming, as well as a strong background in statistical mechanics. Where to apply
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study high quality perovskite materials, obtained by vacuum growth, and controlled in-situ with extremely sensitive characterization techniques. The candidate will conduct experiments based on optical
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well as decentralized machine learning algorithms for large-scale clouds with dynamique parameters. -- Conception of machine learning algorithmes for resource allocation -- Numerical experiments -- Drafting research