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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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-operando spectroscopic techniques Molecular Gate (Spain) – advanced methods to image doping profiles Forschungszentrum Jülich (Germany) – biocompatibility assessment The work will be done in close
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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take place in the glass value chain. Additionally, interfaces for making use of the ontologies in other tasks should be studied, such as the assessment of reuse and recycling potential of specific glass
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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articles, that will be part of your PhD thesis, present your work at scientific conferences and project meetings, as well as internal and external seminars. As a PhD student, you devote most of your time to
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energy, energy storage, electronics, medicine, sustainable manufacturing, etc. The main focus for the advertised position is novel machine learning method development. However, you will be part of a larger
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First Stage Researcher (R1) Country Sweden Application Deadline 29 Sep 2025 - 22:00 (UTC) Type of Contract To be defined Job Status Full-time Is the job funded through the EU Research Framework Programme
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-solving skills, a proactive and enthusiastic approach to engineering research, and adaptability. Proficiency in English, both written and spoken, is required. Great emphasis will be placed on personal
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cancer. The goal will be to find genetic prediction models to be able to predict which childhood cancer patients have a high or low risk of toxicity in childhood cancer. Preliminary the doctoral project