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organelles, and macro-complex structures in their cellular context (in situ structural biology). The laboratory Cell and Plant Physiology (LPCV), which aims to understand the adaptive response of microalgae
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motivated Postdoctoral researcher (W/M) to join our Neuroengineering Laboratory at EPFL (https://go.epfl.ch/ramdya/ ) to work on an ERC funded project aiming to reverse-engineer insect limb motor control
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statistics and standardize complex economic and ecological data into index-based scores for all Minnesota lakes greater than 10 acres. ● Coordinate with team members to integrate these ecosystem service values
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-superalloys” (https://doi.org/10.1016/j.scriptamat.2025.116761 ) to achieve top scientific outputs (e.g. Ti https://doi.org/10.1016/j.scriptamat.2017.06.038 , W https://doi.org/10.1016/j.apmt.2021.101014 , Fe
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agricultural economics who can translate complex policy challenges into rigorous research programmes, lead multidisciplinary teams, and communicate findings clearly to decision-makers. You bring deep knowledge
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temporal resolution. Background in network science, statistical physics and complex systems. (Optional) Familiarity with information-theoretic measures (Transfer Entropy / Symbolic Transfer Entropy) and the
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NetworkX, igraph, and Gephi for complex network and graph-based data exploration; Foundational understanding of computational social science and social network analysis; Expertise in social media mining and
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transformer-based models (e.g., BERT, GPT); Familiar with social network analysis (SNA) using NetworkX, igraph, and Gephi for complex network and graph-based data exploration; Experience with research grant
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of the project is to build a secure by design AI inference stack using the latest advancements in AI, security, and formal methods. Modern AI inference systems combine complex software components, including model
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regulation, with the long-term goal of enabling more precise design of RNA therapeutics for complex diseases. The doctoral researcher will analyse large-scale single-cell and bulk multi-omics datasets