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Field
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hands-on experience with data from animal communication can be acquired on the job but require willingness to learn. The ability to work in a diverse, multi-national team is required. In particular
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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learning for dementia prediction. see here: https://ai-med.de/wp-content/uploads/2025/04/PhD_Position.pdf PhD Position: Interpretable Models for Dementia Prediction Lab for Artificial Intelligence in Medical
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-karriere/jobs/english/PH-2502-AG-Kampfrath-WiMi-ACEs_E.html To learn more about the Kampfrath research group, please visit http://www.physik.fu-berlin.de/en/einrichtungen/ag/ag-kampfrath
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
biology, (bio-)chemistry, and biophysics and have great interest in interdisciplinary research with an eagerness to learn and develop new approaches. Candidates should hold a Masters or equivalent degree in
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta