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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation
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Technopole supports career development through training, mentoring and dedicated learning opportunities. What you'll bring Essential PhD Degree in a relevant scientific field (e.g. genetic epidemiology
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. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required
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of the following methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages
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constitutive androstane receptor (CAR) as models. PXR and CAR transcriptionally regulate cytochrome P450 3A4 (CYP3A4) and CYP3A5-drug-metabolizing enzymes that metabolize more than 50% of clinical drugs
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machine learning techniques, will quantify groundwater recharge and groundwater resilience. Your responsibilities: Analyse the dynamics of hydrological connectivity of soil moisture using gridded soil
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machine learning methods are a plus. Qualifications: PhD in neuroscience, or related fields DeepLabCut or similar methods Demonstrated hands-on experience with 2-photon imaging techniques Experience
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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly