93 machine-learning "https:" "https:" "https:" "https:" "https:" uni jobs at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
quantitative field. Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI
-
will also profit from the vibrant research community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in
-
cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
-
, or methodologies in bioengineering. This search has a particular focus in immunology, neuroscience, and/or computational science/machine learning. That said, we give high priority to the overall originality and
-
, machine learning, and data-driven modeling methods, physiology, transport, fluid and solid mechanics, systems analysis, circuit prototyping, technology transfer, and biomedical design practices, in
-
of California, Santa Cruz, invites applications for a UC Cooperative Extension (UCCE) Specialist at the Assistant rank. For full description, please follow https://recruit.ucanr.edu/JPF00368 This position will
-
processing, neuromorphic engineering, or a closely related field. A solid background in machine learning is expected, with interest or experience in spiking neural networks, temporal modeling, or bio-inspired
-
Caribbean populations of African descent. Most mechanistic insights derive from non-representative cohorts, limiting biomarker discovery and therapeutic precision. Recent multi-omic and machine learning
-
, machine learning model applications, and real-time applications Opportunities to learn more about systems neuroscience and neuroengineering One on one mentorship with graduate students and postdocs
-
to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience