Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
learning university. In this competition, experience in research and/or development in Large Language Models (LLM) and their respective applications is valued. Where to apply Website https
-
There are many additional perks & programs with the CU Advantage . Qualifications: Minimum Qualifications: Bachelor’s degree in computer science, management/computer information systems, computer engineering
-
Computer Programming · Ethics and Policy Questions: Genomics, Healthcare and Big Data · Introduction to Applied Data Analysis · Advanced Concepts in Computer Programming
-
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
-
astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
-
and Data-Driven Discovery, which involves creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection. The second pillar
-
Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
-
Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
-
, through open data, open code, open educational resources, and practices that support replication. Desirable: F1 Experience of leading and teaching large postgraduate taught courses. F2 Experience
-
. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using convolutional neural networks (CNNs) trained on simulated data, enabling faster and