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qualifications with a university degree (Master or equivalent) in physics, engineering or computer science Proficiency in computer vision and machine learning with Python Knowledge of X-ray physics and laboratory
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science or Physics You have developed a keen interest in medical imaging physics, signal and data processing Very good programming skills (C, Matlab/Python, TensorFlow/PyTorch) and a passion for both theoretical and
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with spatial transcriptomics is a strong plus. Excellent scripting and data analysis skills in R and/or Python, with in-depth knowledge of relevant Bioconductor/scverse packages. Strong experience with
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to learn, computational analysis of large sequencing datasets (Python or equivalent); experience with machine learning is a plus Independent, careful experimental work combined with the ability
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research tackling questions on the perception of time and space in virtual immersive environments Solid quantitative skills (R, Python, or Stata) Programming ability relevant to your target position: hands
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virtual environments Strong quantitative skills (R, Python, or Stata; experience with machine learning or advanced experimental methods is a plus) Familiarity with VR-related toolkits (e.g., Unity, Unreal
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candidate has strong analytical skills and programming experience in Matlab, Python, C/C++, or equivalent, and is able and/or eager to develop and implement signal-processing algorithms in such a programming
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already have experience working computationally and developing scientific software Experience in Python is highly recommended, additional knowledge of performance-oriented modeling frameworks, either based
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multiple tasks and deadlines Be a team player with strong communication skills Have experience with coding and modeling (e.g., with Python) Be fluent in spoken and written English Not required, but a plus to
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quantitative and programming skills Strong programming skills in Python, MATLAB, R, or comparable languages are essential, together with experience writing clean, well-documented, and reproducible analysis code