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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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Postdoctoral studies in single-cell and computational biology Do you want to contribute to top quality medical research? A postdoctoral position is available in the laboratory of Professor Francois
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Postdoc (f/m/d) in Irradiation Tolerance of Additive Manufactured Ferritic/Martensitic Steels for...
. Ferritic/martensitic steels, especially the Eurofer alloy specifically developed for fusion applications, are among the most promising material candidates. To enable the fabrication of complex geometries and
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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We invite applications for a NIH-funded postdoctoral researcher position in our computational lab at UMass Chan Medical School. We develop methods to reconstruct multi-modal, condition-dependent
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microstructural disorder, with the aim of uncovering how microstructure geometry fluctuations, heterogeneity, and collective behavior influence damage initiation and crack propagation. The position will focus
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focuses on combining novel genome engineering tools (e.g., CRISPR-based) and computational algorithms to enable regenerative cell therapies. Now, we are seeking a highly driven postdoctoral researcher
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division 8.5 Planning, performing, and evaluating in-situ/4D computed tomography experiments Developing software for the quantitative evaluation of various image data sets (algorithms for detecting volume
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computational approaches to uncover novel biomarkers and therapeutic strategies for CNS disorders. Key Responsibilities: Develop and implement algorithms for multimodal image fusion, combining data from MRI, PET
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and