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) or neural network-based methods. The level of the targeted problems will require further mathematical and algorithmic developments over the current state of data-driven SSM reduction. The PhD position will
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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to muon g-2 from lattice Quantum Chromodynamics and algorithmic developments for multi-level and RG-improved simulations (research group of Urs Wenger) C.) Study of multi-hadron systems, with a focus on
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Our research group focuses on the development of AI algorithms for industrial applications. The main scope of our activities is the optimization and automation of workflows and production systems
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algorithms and AI-based solutions for data processing and validation, and provides scientific expertise for the implementation of future remote sensing missions. The team’s work bridges earth observation with
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; hands-on with Illumina and/or Nanopore and meta-omics is a plus • Strong organizational skills, attention to lab detail, and ability to work independently • Willingness to conduct short-term fieldwork in
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scientific questions or develop algorithms that perform automatically some challenging tasks. This typically involves exchanging actively with collaborators and domain experts to understand the precise
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using learning-based algorithms Development of a simulation framework for stressor analysis and traffic equilibrium modeling Integration of predictive analytics and multi-agent reinforcement learning
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fungal ecology, (meta-)genomics, bioinformatics, and multivariate statistics. Experiences in forest ecology and biogeochemistry are welcomed but not required. You are highly self-motivated and committed
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for monitoring people’s health. You will focus on developing new solutions, electronics, algorithms and methods to assist individuals, physicians and sports coaches to effortlessly and continuously monitor health