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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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computing and reproducible research. (Optional) Experience in time-series analysis, with explicit attention to intraday temporal resolution. Background in network science, statistical physics and complex
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-supervised by Prof. Jenny Nelson (Imperial College London), and Dr. Riccardo Rurali (ICMAB-CSIC). Main tasks: • Develop atomistic and coarse-grained models of the structure and dynamics of molecular
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certain type of treatment, with potential impact in clinical practice and cancer research. This position is linked to the project BIOEURECA:Next-gen biomarker discovery: foundation models for tissue spatial
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for a short-term 2.5-month postdoctoral position (full-time) focused on developing and validating machine learning models that predict soil health and crop performance. The position will exploit datasets
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of resistance to targeting therapies (antibodies, and antibody-drug conjugates) integrating clinical data, 3D cancer cell models, high-throughput approaches and co-culture systems. The successful candidate will
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. The successful candidate will be joining the Ultracold Quantum Gases group led by Prof. Dr.Leticia Tarruell . The Fermi-Hubbard model is a cornerstone model of condensed matter physics. It describes the physics
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automated liquid-handling workflows on an Opentrons Flex platform (Python API). Establish and run spatial proteomics on in vitro co-cultures, engineered 3D tumor models and tissue sections. Implement clonal
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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine
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/Qualifications - Demonstrated expertise in advanced statistical techniques, such as: Multilevel modeling (HLM, mixed-effects models) Longitudinal and panel data analysis Structural equation modeling (SEM) Causal