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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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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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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
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, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting, as is experience with single-cell RNA sequencing or other omics assays
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(data assimilation, machine learning, etc.) Writing proposals / securing external research funding Writing and submitting scientific papers Leading a research group Supervising students Participating in
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of these patients. The goal of this project is to combine cutting-edge multi-omics technology, data analytics, machine learning and clinical samples from the human eye to decipher new insights into disease mechanisms
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted