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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=26749 PhD position in biomedical research (m/w/d) Stellenanzeige merken Stellenanzeige teilen wanted
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, epidemiological, and environmental data Taking part in developing and validating predictive cancer‑risk models Contributing to spatial analysis and data integration in geographic information systems (GIS
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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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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; 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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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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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 22 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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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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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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. 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