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to learn A background in renewable energy, e-mobility, or engineering Good programming skills (e.g., Python, MATLAB or R) Familiarity with control concepts Ability to work effectively with data and applied
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, TensorFlow) and protein language models. Experience with programming (Python preferred) for bioinformatics or data science applications. Exposure to degradomics methods or post-translational modification
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programming languages such as C/C++, Python, Julia or similar. Experience with mathematical optimization software. Experience with machine learning packages. Excellent communication in English and interpersonal
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start) Due to engagement with participants and stakeholders, the PhD student must be fluent in Danish. Programming skills (e.g., Python) will be considered a strong advantage, particularly for handling
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) Programming experience (e.g., Python, Julia) Strong analytical thinking and problem-solving abilities Interest in power and energy systems Excellent English communication skills and a collaborative mindset to
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and Computer Vision Excellent programmer in Java / C / Python or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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• Knowledge of statistics and/or data science • Knowledge of Matlab, Python, or a similar programming language • Some research experience (e.g., in the form of a conference contribution, a publication, or a
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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, Management Science, Analytics and Data Science Knowledge of and practical experiences with programming (e.g., Python, Java, Julia, etc.) You must have a two-year master's degree (120 ECTS points) or a similar
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deep learning, preferably including some exposure to graph neural networks or geometric deep learning. Proven experience with implementing machine learning methods in Python and Pytorch. Familiarity with