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to explore GNSS Reflectometry (GNSS R) as a novel, low cost, low power bistatic remote sensing technique optimized for nanosatellite platforms. GNSS R leverages signals of opportunity from existing
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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release of hydrophobic drugs from lipid nanocarriers. The candidate will hold a M.Sc. in pharmacy and preferably has previous experiences with scientific programming (e.g. Matlab, R). The candidate must be
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, molecular biology, ecology or closely related field is required for the PhD stipend. Strong competences in molecular laboratory work (e.g. eDNA analysis). Proficiency in programming in R, utilization
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laboratory work (DNA extraction, PCR, sequencing workflows) Bioinformatics and statistical analysis (e.g., R or Python) Biodiversity assessment or ecological community analysis Fieldwork experience in
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proficiency in Python, R, or MATLAB. Experience with Deep Learning frameworks (PyTorch, TensorFlow) and LLM APIs is an asset. Communication: Fluent English skills, both written and spoken, with a demonstrated
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modeling Programming/scripting skills (e.g., Python, R) for data handling and visualization Strong interpersonal and cross-disciplinary collaboration skills What We Offer Innovative Research Framework
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data science. You must be curious and driven with excellent interpersonal skills and writing competencies. Experience with programming languages, notably Python or R, is expected. The work will imply
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, Python and/or R, and ability to manage and structure large datasets is essential. Interest/skills in application of AI methods to clinical data is an advantage. Stipend 2: Genetic Risk Communication and
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skills, Experience in programming in Python or another language, e.g., in C++, Matlab, R, Familiarity with basic concepts of dynamical systems, Knowledge of wind turbine dynamics is a plus, Curiosity to