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management systems Strong track record of scientific publications Excellent communication skills in English (oral and written) Who we are The Section for Electrical Energy Technology focuses on energy system
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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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analysis of the ATLAS experiment data. The L2IT team plays a driving role within the ATLAS collaboration for the reconstruction of charged particle tracks using geometric deep learning (GDL). The person who
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and crystallography* Technical expertise molecular biology, protein production, biophysical and biochemical techniques and structural analysis* Track record of writing papers as evidenced by
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to support system-level navigation development. • Track progress toward defined project milestones and contribute to technical reports and project updates. • Prepare manuscripts, presentations, and other
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experience in B and T cell immunology, or virology / microbiology and a proven research track record. Strong communication skills and a good command of spoken and written English are essential. Applicants
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within the project. REQUIREMENTS: a doctoral degree (PhD) in biotechnology or a related field, such as biology or medical laboratory science, a documented scientific track record, including at least one
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., RDoC, HiTOP) Interest in translational or clinically relevant applications of computational psychiatry Prior experience working with large-scale datasets Strong scientific writing skills and a track
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learning methods, with strong analytical and programming skills (e.g. R or Python) Experience of undertaking accurate, reproducible data preparation and analysis A track record of peer-reviewed research
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environment that integrates student training with project deliverables. Project Management and Communication – 10% Coordinate research activities, track progress toward project milestones, and maintain