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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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or development of machine learning methods, or a desire to learn these skills, are also welcome. We offer the opportunity to work on interesting scientific challenges using modern experimental methods available in
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and