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Field
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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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Classification Title: PhD in Bioinformatics, Computational Biology, Biomedical Engineering, Data Science, or a related field. Classification Minimum Requirements: PhD in Bioinformatics
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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Requirements: PhD in Biomedical Engineering, Electrical Engineering, Neuroscience, Computer Science, Psychology, or closely related field Demonstrated MEG analysis experience (FieldTrip/Brainstorm/MNE-Python
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System Modelling group at TUM (https://www.asg.ed.tum.de/esm/home/) and will be closely involved in the Schmidt Sciences project MountAInWater, coordinated by the Institute for Science and Technology
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section Participate in experimental campaigns at the European XFEL About you: PhD or equivalent experience in physics, engineering, or a related discipline is required Strong skills in numerical modeling
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness