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Field
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | 1 day ago
supervision Yes Research training / discussion Yes Career advisory services and programmes for future professionals The BTU Career Center (https://www.b-tu.de/en/careercenter ) offers extensive support to
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organizational aptitude round out the ideal profile. We offer you • a thorough induction into your area of responsibility • a diverse range of training and development opportunities for professional and personal
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round out the ideal profile. We offer you • a thorough induction into your area of responsibility • a diverse range of training and development opportunities for professional and personal development
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(department “Geophysics”), we are looking for a: Reference Number 11003 In the research group "Magnetotellurics", we have been working for several years on the further development of the high-frequency Radio
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. Correlated experimental, ab initio and multi-scale techniques are central to our mission: Development and application of advanced simulation techniques to explore and identify the fundamental structures and
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opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs 30 days of annual leave Further development of your personal strengths, e.g. through an extensive range of training courses; a
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, only symptomatic treatments exist currently. A key problem is that drug targets have been proposed, yet, it has proven very challenging to translate these into drug development programs. Dysfunction
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Help us shape the energy transition! As part of your doctoral studies, you will support the development of innovative