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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
shape the next generation of agentic AI tools for biomedical research. A highly interdisciplinary environment connecting AI, computational biology, human–computer interaction, and research software
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Quantum Fundamentals, ARchitectures and Machines program (Q-FARM) is an interdisciplinary initiative woven throughout the university. Q-FARM harnesses the expertise and facilities of Stanford University and
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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structure and quantum chromodynamics, 2) Experience in the use of machine learning and high-performance numerical computations, 3) Readiness to teach courses in physics and computer science in English
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perform experiments using an array of state-of-the-art techniques from systems neuroscience, genetics, and physiology. More information about this lab can be found on his website https://knightlab.ucsf.edu
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environments, and assessing model explainability. You'll work closely with a team of graduate students, postdocs, and other collaborators to develop innovative AI models, create software tools, and establish
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assimilation, machine learning, and optimization techniques. Experience in student mentoring. Publications in leading journals within the field. Preferred Qualifications PhD in Environmental Modeling. More than
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to detail. Proactive and self-motivated mindset with an eagerness to learn and grow Excellent computer skills with demonstrated proficiency in Microsoft Office Suite. Please include a cover letter detailing
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physiology is an asset Experience in statistics and data analysis of large data sets (including time series analyses and machine learning) Good programming skills Very good communication and interpersonal
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cybersecurity expertise with modern AI techniques such as machine learning, deep learning, or large language models? Then we strongly encourage you to apply. You will join an established team with 25+ members