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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors
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techniques to challenging biological systems. Requirements: Ideal candidates should hold an excellent PhD in structural biology, biochemistry, biophysics or in an equivalent area. Experience in membrane
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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lab in Stanford’s Psychiatry Department, led by Neir Eshel, MD, PhD. We are looking to hire curious and ambitious postdocs to join our team. Lab projects focus on the neural circuitry of reward-seeking
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biological robustness to join our team. Required: You possess a PhD or are close to completing your PhD in cell biology, developmental biology, stem cell biology, or a closely related field. You have strong
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neurodegenerative diseases. The position is open to candidates with PhD degree and previous postdoctoral experience and offers the opportunity to work in an excellent team engaged in mouse models of disease
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on reliability, security, and resilience of electric power systems and microgrids and stability analysis and Scientific Machine Learning (SciML) for microgrid applications. The successful candidate will be
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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composition Present your results at international conferences and publish in peer-reviewed journals Your Profile: A Masters with subsequent PhD degree in chemistry, physics, environmental science, atmospheric
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to start on or prior to September 1, 2025. Minimum Position Qualifications An earned PhD or equivalent in any area of biology or related field is required, with a preference to candidates trained in an area