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learning and deep learning models for trait prediction and climate-resilient wheat breeding. Analyze time-series UAV data using crop models in combination with genomic and agronomic information. Collaborate
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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, knowledge-driven models and AI-based decision support can be integrated to support resilient and energy-aware manufacturing systems. Special emphasis will be placed on multi-objective optimization, learning
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levels of curiosity, independence, and excellent organisational skills High levels of critical thinking, problem solving, and a general drive to learn new concepts and methods Advanced skills in
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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required — we value biological curiosity and willingness to learn. Have good teaching abilities. Have awareness of diversity and equal opportunity issues, with specific focus on gender equality. Great
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
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university and public authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact information to trade union representatives. To apply
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to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact