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research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong communication skills in English and good knowledge of French
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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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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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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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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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helium droplet machines. Also, you will be jointly responsible for making sure that various experimental apparatus in the laboratories are maintained and serviced with timely care. In particular
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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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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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