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Field
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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rehabilitation center, and will be embedded in the Vision and Imaging Data Analytics group at the Department of Intelligent Systems and Centre for Cognitive Science and Artificial Intelligence at Tilburg
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- work environment: Research Laboratory. - main mission: The goal is to design and prototype a Reciprocating Expansion Machine with a Linear Alternator (REMLA – 200W–100Hz – 10mm stroke), coupled with
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, molecular biology, or related disciplines FELASA certification Experience with or highly interested in experimental immunology (e.g. in vivo models, flow cytometry, imaging) Motivation to learn and apply
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concerned, among other things, with the effects of images and the mechanisms that make up communication with images. Key questions are: Who produces images and for whom? What message do these images convey
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cell biology, and experience working with plants are pluses. We offer training in advanced imaging, and various molecular & genetic skills for investigating fundamental cellular mechanisms. The lab is
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-studies/. Background and description of tasks PhD project 1: The PhD project involves research using invertebrate model systems to investigate the mechanisms by which potential host genome editing processes
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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of an image analysis algorithm for particle tracking and speed quantification. Requirements for candidates: Essential: BSc and MSc in biochemistry, biology, biophysics, biotechnology, biomedical engineering
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domains with advanced methods development. Various brain imaging methods are employed to describe or manipulate behaviour, including (intracranial) EEG, functional MRI, and non-invasive brain stimulation