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Field
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are: i) to develop a new selected CI algorithm allowing reaching chemically-accurate results for large compounds; ii) to extend the currently-available database to properties relevant for both core
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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cellular level. They involve developing analytical methods and software tools for large, domain-specific, and heterogeneous data to understand cellular processes and apply findings in medicine and
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nuclear and particle physics research leveraging machine learning and AI for data analysis and detector development, as well as exploratory work in quantum algorithms, depending on background and interests
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Control for System Engineering department includes the development of methods and tools for optimizing and controlling the dynamic behavior of systems in a wide range of application domains, in
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Description of work You will be working in the laboratory of Marta Bally (https://ballylab.com