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requirements for admission to the PhD programme Experience implementing and modifying deep learning architectures. Working knowledge of Python and a modern deep learning framework. Strong programming skills in
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. This staged approach will allow the student to determine both the capabilities and the limitations of the technique rather than assuming in advance that a particular implementation will succeed. MATLAB, Python
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University). The team will consist of Sander Nieuwenhuis, a postdoctoral researcher, the PhD student and a research assistant. If time permits, the PhD student will get the opportunity to contribute
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MS data analysis using software such as FragPipe and DIA-NN, as well as basic programming skills in Python. Practical experience in antibacterial assays based on dilution or diffusion methodologies
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Python. You will work in a newly formed team of two postdocs and a PhD student, led by the PI (Ekaterina Ilin), and collaborate closely with both postdocs, who will be working primarily on a large optical
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, particularly EEG; • Programming experience, particularly in R and Python; • Proficiency in statistical methods commonly used in psychology and neuroscience (ANOVA, linear models, etc.); • Familiarity with
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: The PhD will involve embryo culture and manipulation, microinjection, CRISPR-knockouts, interference-knockdowns, HCR whole-mount in situ hybridization, confocal microscopy, single-cell RNA sequencing
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bioinformatics, computational biology, genomics, evolutionary biology or a related discipline. Essential skills include experience with genomic data analysis, proficiency in Python and/or R, familiarity with
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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characterization of materials’ magnetic, electronic and structural properties. Experience in spectroscopy and microscopy is valuable Knowledge in programming (Python) and 3D modelling will be valuable Availability