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Field
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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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Qualifications: A PhD, MD/PhD, or equivalent research doctoral degree in neuroscience, biomedical data science, computer science, psychology, psychiatry, statistics, engineering, applied mathematics, or a related
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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Responsibilities Conduct research in computational methods for environmental and engineering applications. Develop and analyze numerical algorithms, reduced-order models, and machine-learning-enhanced simulation
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modern machine learning and a strong record of research accomplishment who are excited to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
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relevant subject area: Machine Learning, NLP, Computer Vision or related fields. Strong publication record in AI/ML/CV or related areas. Ability to work independently and collaborate across disciplines
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23 Jul 2026 Job Information Organisation/Company ETH Zürich Research Field Engineering » Biomedical engineering Engineering » Chemical engineering Engineering » Materials engineering Neurosciences
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, biophysics, applied mathematics, or related field. Expertise in machine learning, computer vision, or image analysis. Experience with data management and databases. Experience working with Python, scientific
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
across European life-science AI efforts. Requirements PhD in computational biology, bioinformatics, machine learning, or a related computational field Hands-on experience with foundation models / large
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project