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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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vision, IoT sensors, and blockchain to monitor food quality, safety and animal welfare in real-time and enhance transparency. AI and machine learning will analyse data from pilot sites to identify
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged
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Researcher to join our team in reimagining how we discover and deploy drug combinations in the clinic. Our work is highly interdisciplinary, integrating high-throughput screening, state-of-the-art machine
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
collaboration between the Department of Electrical and Computer Engineering and the Novo Nordisk Foundation CO2 research center, Aarhus University, we aim to address this opportunity by developing digital twins
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approach to develop chemical probes, investigate biological mechanisms, and evaluate in vivo efficacy. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR
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. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR) as models. PXR and CAR transcriptionally regulate cytochrome P450 3A4 (CYP3A4) and CYP3A5-drug
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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods to uncover underlying biological