Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
docking and machine learning. Among the key duties of the position are the following: Performs various research and technical operations relative to ongoing investigatory activities of a laboratory; may
-
. Research opportunities will focus on the use of novel modeling tools for hydrology and water resources systems, with an emphasis on machine learning and remote sensing, with a focus on developing detailed
-
our team, as well as a willingness to learn new methods and techniques as the projects evolve. This is a one-year full-time appointment with the potential for extension contingent upon successful
-
scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
-
into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
-
in Dr. Shanlin Ke’s lab. The overarching goal of Dr. Ke’s lab is to develop computational approaches and leveraging bioinformatics tools, metagenomic sequencing, multi-omics data, machine learning, and
-
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
-
, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
-
in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
-
well as bulk RNA-Seq, Proteomics, and Metabolomics generated from mouse and patient cohorts with rich clinical data - Advanced modeling of arrhythmias using generalized linear models and machine learning