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and training machine and deep learning models using Keras/TensorFlow and/or PyTorch, supported by experience in statistical data analysis. You have experience with collaborative coding practices
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and refactoring source code for computational and data science applications both on the methodological and implementation side or deploying and integrating applications to adequate compute environments
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; working knowledge with research data management according to the FAIR principles and corresponding software systems (e.g., openBIS); hands-on experience with best practices for code management and
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such as Stata, R, and/or Matlab Collecting data using web scrapers and data providers like Macrobond Coding in R or Matlab on data analysis and macroeconomic forecasting Programming surveys with Qualtrics
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, you will: Design and perform fluorogenic and nano-photonic DyeCycling experiments. Write/adapt analysis code to process fluorescence trajectories and extract kinetic information. Evaluate bioconjugation
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referees who can provide professional references (We will contact them as we review the applications). Portfolio – Overview with project summaries and links to code repositories, datasets, and other
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datasets, code, experience around tomographic measurements of battery materials and components, image processing, and multiphysics simulation. During their study, doctoral students are encouraged to pitch
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techniques or tooling, such as relevant Python or Julia libraries (PyTorch, JAX, and similar) and how to package simulation or ML codes for dissemination or deployment on distributed servers (e.g., Docker
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code, clear README, etc.). We will provide you with guidance for the implementation, but independent work, in general, is required and appreciated. The workload consists of up to 15 hours per week during
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timelines, risks, and align milestones across academia and industry. Supervision & mentoring: Co-supervise MSc and PhD students; run code/design reviews; foster a collaborative, high-standards research