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Predoctoral Researcher (PhD Candidate) in Computational Simulation and AI for Therapeutic Ultrasound
English level • Programming skills in Python and/or MATLAB are desirable Selection will be based on academic excellence, suitability for the project and research potential. Where to apply E-mail
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skills Biomedical Data Science UG degree: List A List B Introduction to Medical Ethics Python language programming Probability theory, statistics and Bayesian analysis Artificial intelligence in Medicine
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R and/or Python, with experience in data integration and statistical analysis. Exposure to RNA therapeutics or functional genomics approaches is an advantage. Strong interest in interdisciplinary
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skills, particularly in Python and/or R · Experience in machine learning and AI · Fluency in spoken and written English · Ability to work independently as well as in interdisciplinary teams Meritorious
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experience or interest in denovo genome assembly using PacBio HiFi data Familiar with R, Python or other coding languages Interdisciplinary communication skills and intercultural competence Excellent knowledge
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PixHawk Autopilot, Arduino boards, Raspberry Pi - or equivalent Experience with ROS/ROS2 Experience with programming languages like Matlab, Python, C++ Familiarity with machine learning and/or deep learning
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research skills (or willingness to develop them), including experience with statistical software (e.g. R, Stata, or Python) Excellent written and spoken English Ability to work independently as
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programming language, preferably Python or R. Experience in any of the following areas: large scale sequence analysis, microbial genomics, human gut microbiota research (shotgun metagenomics), Metagenome
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Python and/or R for bioinformatics and ability to write clean, reproducible, and well-documented code for complex multi-step pipelines. have documented experience from cancer research. Additional
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analysis, and biology, as well as Python and R programming language (critical). Previous experience analyzing high-resolution spatial VDJ/transcriptomics, long-read sequencing, single-cell transcriptomics