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for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background in image processing and analysis, including deep learning
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these properties in collaboration with geodynamicists to create numerical models of planets. We are looking for a highly motivated individual who would want to be part of a five-year research program to quantify
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of biodiversity and model the potential future biodiversity recovery given during land use transformation and restoration in Denmark. This involves spatial and temporal optimisation and prioritisation of land for
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research group “Computational Integrative Biodiversity” (CIB) combines modern computational modeling with biodiversity theory as well as geohistorical and climatological data. We investigate how biodiversity
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in animal models contribute to behavior and disease. The Ideal Candidate will have: A PhD in neuroscience or related discipline Prior experience in studying neural circuits Expertise with at least some
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Postdoctoral position in Bioinformatics/Computational Biology (m/f/d) (full-time position 100 % ~ 38
, contributing to the functional interpretation of genetic variants and the modelling of immunopathological pathways. The position will be closely integrated with other computational biologists
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biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and epigenetic mechanisms
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innovative, interdisciplinary projects. Using both in vitro and in vivo models, the successful candidate will perform functional validation of candidate genes and advance our mechanistic understanding
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for catalysis and electrocatalysis. You will develop a research activity within experimental characterization of electrocatalyst model catalysts using electrocatalysis workstations, scanning probe microscopy and
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device-to-architecture level models of emerging nanoscale devices (spintronic, resistive, or hybrid) for in-memory and neuromorphic computing. Exploring hardware-level security mechanisms based