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, develop, adapt and experiment new methods in the frame of Exposome Booster Program Animal experimentation focused on pulmonary pathophysiology Histology, morphology Lung function Cytometry Molecular biology
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her/his project. He/she shall handle design, plan, perform and interpret various experiments. He/she should master the associated literature. He/she should be able to communicate his/her results in
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opportunities with international partners (University of Tokyo, CIC BiomaGUNE, University of Uppsala). - Co-supervise students and contribute to technology transfer activities within the Inserm innovation
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should be familiar with relevant literature. He/she should be well organized to plan, execute and interpret the experiments. He/she will present her progress at laboratory meetings. He/she will also
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responsibilities: To contribute in experiment design, collection and analysis of data for the teams research projects. To maintain careful permanent records of all experimental work. To maintain appropriate
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-gut-brain axis and to pave the way for a new generation of preventive interventions against AD through microbiota modulation. • Supervise a research project • Design and conduct experiments, including
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hardware design and verification (HDL: VHDL/Verilog, simulation tools). · Hands-on experience with FPGA prototyping or hardware/software co-design is highly desirable. · Good communication
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of London Cancer Institute. Objectives: The aim of the project is to study chromatin remodeling defects in cancer. The successful candidate will be responsible for designing and executing experiments
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coordination, budget monitoring) and promote it in various forms (scientific reports, scientific articles, conferences, design notes for Seaturns company) Duration of contract : 12 months Salary : € 2.750 gross
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 2 months ago
. Test model predictions in behavioral experiments. Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems