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the services nature provides to people. The position will combine ecological data analysis with statistical and spatial modeling to quantify chemical impacts across multiple levels of biological
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 20 days ago
the A20 mouse lymphoma syngeneic model with anti-mouse PSGL-1. The lymphoma microenvironment spatial composition and immune cell activation states will be characterized by advanced techniques. We also plan
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
intersection of (micro)RNA biology, 3D human model systems, nanomedicine, and computational (ML) disease modelling. You will be able to contribute to our vision to translate basic findings into medicinal RNA
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; enrolled in a PhD program or a non-degree course. Experience in: a) ROS, programming in C++ and Python; b) Modelling and control. Work Plan: The work plan is divided into five phases: Phase 1 – Development
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of innovative research methodologies and will independently design, optimize, and execute complex immunologic and molecular experiments across in vitro, ex vivo, and in vivo model systems. Technical scope
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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challenging conditions (see https://tissueresilience.com ). Harnessing powerful in vivo models, our work spans multiple biological scales - linking the molecular cell biology of individual cells to the health
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vivo models, immune–metabolic signaling, flow cytometry, and cell death or redox biology. Experience with single-cell or spatial transcriptomics is highly desirable. Independent yet collaborative
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at various spatial and temporal scales, ranging from individual cells to tissues, organs, and full physiological systems. Computational modeling and simulation: development of constitutive laws, finite-element
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes