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patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which
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have a few projects related to our novel method, AQUADA, which uses thermography and computer vision to detect damage in wind turbines and PV panels. We are looking for a new colleague to further develop
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patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which
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such as data scarcity, cultural sensitivity, inclusivity, and the need for robust preference optimization methods that go beyond standard fine-tuning. Key research objectives include: Developing Efficient
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resource recovery pipelines You will contribute to the translation of structural and biophysical insights into technologies or methods for transforming recovered biopolymers into valuable products or process
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biological problems and is thus not focused on development of computational methods. We wish to generate small protein binders against a range of important neuroscience targets – principally intrinsically
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methods to directly probe the phase behavior, i.e., the number and types of phases, in individual aerosol particles, and to explore how it affects their ability to form clouds. The project will focus
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also have responsibilities related to training other team members in relevant skills, fostering a collaborative and educational atmosphere within the group. The project involves a mixture of method
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of field-grown vegetables or agricultural crops, and master state-of-the-art experimental and analytical methods in plant physiology, agronomy and agroecological research. You are expected to have research
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analytical techniques Advanced knowledge in research design and methods (running extensive experimental studies) Familiarity with practices of transparency and open science Strong interpersonal and