97 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" research jobs at Nature Careers in United States
Sort by
Refine Your Search
-
Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to the development of AI/machine learning approaches for chemical and
-
image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including
-
: https://obgyn.uchicago.edu/research/griffith-laboratory Required Qualifications: Qualifications needed for this position include a PhD in clinical psychology or a related field, such as psychology
-
a postdoctoral scholar in computational biology. The PI, Dr. Lixing Yang is an Associate Professor at the Ben May Department for Cancer Research and the Department of Human Genetics. To learn more
-
fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro
-
or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
-
T cell biology or cancer immunology, and programming skills (R, Python) for data analysis. Please also read recent manuscripts published in the last two years 2024 Nature: (https://www.nature.com
-
publication record in immunology/epigenetics. Information on our postdoctoral training program, benefits, and a virtual tour can be found at http://www.utsouthwestern.edu/postdocs . Please also read recent
-
design and discovery, including the use of artificial intelligence (AI) and machine learning (ML) techniques. The hired candidate will focus on computational aspects of immune repertoire analyses
-
Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights