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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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data. Together with the Artificial Intelligence and Cancer Evolution Division at the German Cancer Research Centre DKFZ, led by Moritz Gerstung, we have recently established a systematic spatial
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be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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Proteomics workflow. For more information: group.szbk.u-szeged.hu/sysbiol/horvath-peter-lab-index.html Your tasks Building large scale foundation models Applying and further developing single cell segmentation
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details of two referees to kaspar.valgepea@ut.ee by January 11, 2026 • Preferred start date: February 2026 • Work location: Institute of Bioengineering, Nooruse 1, Tartu For more information, contact group
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measurements A good understanding of advanced physiological techniques Experience with enzymatic in vitro assays and plant x climate interactions Experience in complex data handling and statistical analysis
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to join a collaborative, diverse, and creative research team. Experience in molecular biology, data analysis, and animal experiments is an advantage. The successful candidate will apply molecular and
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Are you interested in neuromorphic spintronic and can you contribute to the development of the project? Then the Department of Electrical and Computer Engineering invites you to apply for a one year
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increasing independence over time. Collaborate on project and analysis design guided by their PI. Develop new computational methods. Adhere to field and lab standards for data analysis. Identify, process