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to magnetosphere-ionosphere coupling; experience with magnetometer and incoherent scatter radar data is particularly welcome. Excellent skills in Python programming for space science applications; experience with
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of neural network architectures (as a plus: PINNs, neural operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing
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characterization with scanning electron microscopy is a plus Experience with computational tools such as MATLAB, Python or relevant tools Ability to work independently and collaboratively in a multidisciplinary team
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Qualifications Ph.D. in Bioinformatics, Computational Biology, Systems Biology, or a related field Proven experience in the analysis of single-cell or spatial omics datasets Strong programming skills in Python and
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(e.g., R, Python). Proven ability to publish at a high international level. It is a prerequisite that you are good at communicating in English. Strong collaborative skills and good collaboration skills
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, Python) and programming An ability to collaborate within teams and across disciplines An ability to take initiative, develop and manage research activities An ability to communicate effectively in English
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and conducting laboratory work. Insight into applied mathematics, linear algebra, process-based modeling, and soil health indicators. Experience with Python, applied statistics, and gradient-based
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evaluations Solid programming skills in at least one relevant language (e.g., Python, C++) Interest in interdisciplinary work involving engineering, sustainability, and real-world infrastructure Strong written
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. Your competencies We thus imagine that you: have a strong background in digital signal processing and machine learning; have substantial experience with scientific computing in Python/C++/ROS; know
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Computer Science and Mathematics. A background in algorithm design and implementation combined with solid programming skills (e.g., Java, C, C++, Python), is highly valued. Candidates are expected to contribute